A mental model is what the user believes about the system at hand.
“Mental models are one of the most important concepts in human–computer interaction (HCI).”
— Nielsen Norman Group
A mental model is what the user believes about the system at hand.
“Mental models are one of the most important concepts in human–computer interaction (HCI).”
— Nielsen Norman Group
How do we engage in bottom-up whole system change? Perhaps we need a model for understanding who we are serving that transcends the bias and limitations of personas as they are used in user experience design (UX).
What is a more holistic model for understanding human perceptions, motivations, and behaviours?
The models are developed in Python [46], using the Keras [47] and Tensorflow [48] libraries. Detailson the code and dependencies to run the experiments are listed in a Readme file available togetherwith the code in the Supplemental Material.
I have not found the code or Readme file
Top 8 SaaS Pricing Models: Ultimate Guide for 2021Alina NechvolodE-Commerce & SaaS StrategistSaaSHomeBlogEntrepreneurshipTop 8 SaaS Pricing Models: Ultimate Guide for 2021Oct 28, 202016 min readThere’s hardly a thing that impacts your software-as-a-service product revenue more than SaaS pricing models. Still, for many companies choosing the right monetization strategy is no easy feat. To shed some light on this matter, we have prepared a detailed guide on the most popular SaaS pricing strategies. You will find out the pros and cons of each option and learn how to adopt them properly from well-known SaaS companies. Finally, we will discuss the required steps to take when choosing between different SaaS business models.
There’s hardly a thing that impacts your software-as-a-service product revenue more than SaaS pricing models. Still, for many companies choosing the right monetization strategy is no easy feat.
To shed some light on this matter, we have prepared a detailed guide on the most popular SaaS pricing strategies. You will find out the pros and cons of each option and learn how to adopt them properly from well-known SaaS companies.
Finally, we will discuss the required steps to take when choosing between different SaaS business models.
Impact of Social Sciences. “How Models Change the World – and What We Should Do about It,” August 20, 2021. https://blogs.lse.ac.uk/impactofsocialsciences/2021/08/20/how-models-change-the-world-and-what-we-should-do-about-it/.
subtle knowledge constructs, modeling languages, elicitation, and validation processes
Creating a community network ontology is therefore about much more than just knowledge representation. It also requires us to think about how this conceptual knowledge model affects real-world knowledge creation and application processes, in our case concerning participatory community network mapping. Its participatory nature means that we need to think hard about how to explicitly involve the community in the construction, evolution, and use of the ontology.
wealthe
Winthrop believed that the acquisition of wealth and profit was acceptable so long as it was done in the glory of God and for the common good. In other words, he justified the acquisition of wealth in a religious society that it was the duty of members in a society to band together to correct the inequality put forth by God. The act of charity was portrayed as a service to God. He also believed that excessive wealth lead people astray from God.
Winthrop addressed wealth in "A Model of Christian Clarity" because he called for members of his community so they could establish successful colonies in the face of numerous hardships. This was because many were not willing to share their wealth with others or cooperate. He wanted to place the interests of the community over the interests of the individual.
Citations: Wood, Dr. Andrew. “Summary of John Winthrop’s ‘Model of Christian Charity.’” San Jose State University COMM 149 Rhetoric and Public Life, www.sjsu.edu/faculty/wooda/s149/149syllabus5summary.html. Accessed 8 Sept. 2021.
Personalized ASR models. For each of the 432 participants with disordered speech, we create a personalized ASR model (SI-2) from their own recordings. Our fine-tuning procedure was optimized for our adaptation process, where we only have between ¼ and 2 h of data per speaker. We found that updating only the first five encoder layers (versus the complete model) worked best and successfully prevented overfitting [10]
Constant, A., Conserve, D. F., Gallopel-Morvan, K., & Raude, J. (2021). Acceptance of COVID-19 preventive measures as a tradeoff between health and social outcomes. PsyArXiv. https://doi.org/10.31234/osf.io/ytz8p
hyper-parameters, i.e., parameters external to the model, such as the learning rate, the batch size, the number of epochs.
Sadus, K., Göttmann, J., & Schubert, A.-L. (2021). Predictors of stockpiling behavior during the COVID-19 pandemic in Germany [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/2m9nu
Wang, B., Gou, M., Guo, Y., Tanaka, G., & Han, Y. (2020). Network structure-based interventions on spatial spread of epidemics in metapopulation networks. Physical Review E, 102(6), 062306. https://doi.org/10.1103/PhysRevE.102.062306
ReconfigBehSci on Twitter: ‘I just had cause to revisit the Friston modelling paper from Sept: Https://t.co/QOTC8fXV0n 1/n’ / Twitter. (n.d.). Retrieved 26 February 2021, from https://twitter.com/SciBeh/status/1336277391233208320
Lee Kennedy-Shaffer. (2021, May 6). .@rebeccajk13 and @mlipsitch showed how to use that to estimate efficacy against prevalent infection (https://t.co/LeXxqcumGS). [Tweet]. @LeeKShaffer. https://twitter.com/LeeKShaffer/status/1390322501817880581
Yasseri, T., & Menczer, F. (2021). Can the Wikipedia moderation model rescue the social marketplace of ideas? ArXiv:2104.13754 [Physics]. http://arxiv.org/abs/2104.13754
Ingale, M., & Shekatkar, S. M. (2020). Resource dependency and survivability in complex networks. Physical Review E, 102(6), 062304. https://doi.org/10.1103/PhysRevE.102.062304
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Romero, P., Mikiya, Y., Nakatsuma, T., Fitz, S., & Koch, T. (2021). Modelling Personality Change During Extreme Exogenous Conditions. PsyArXiv. https://doi.org/10.31234/osf.io/rtmjw
A Historical 3D Model:
How can we tell what software or platform is used such as for this historical site? https://www.annefrank.org/en/anne-frank/secret-annex/
Iacob, C. I., Ionescu, D., Avram, E., & Cojocaru, D. (2021). COVID-19 Pandemic Worry and Vaccination Intention: The Mediating Role of the Health Belief Model Components. Frontiers in Psychology, 12, 674018. https://doi.org/10.3389/fpsyg.2021.674018
Padilla, L., Hosseinpour, H., Fygenson, R., Howell, J., Chunara, R., & Bertini, E. (2021). Effects of COVID-19 Uncertainty Visualizations on Novice Risk Estimates. PsyArXiv. https://doi.org/10.31234/osf.io/6axc7
Palminteri, S. (2021). Choice-confirmation bias and gradual perseveration in human reinforcement learning [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/dpqj6
has been adapted to thecomputational model in [14]; it uses type annotations to help the proo
Crocker-Buque, T., & Mounier-Jack, S. (2018). Vaccination in England: A review of why business as usual is not enough to maintain coverage. BMC Public Health, 18(1), 1351. https://doi.org/10.1186/s12889-018-6228-5
the Guardian. “How Good Are We at Predicting the Pandemic? | David Spiegelhalter & Anthony Masters,” May 9, 2021. http://www.theguardian.com/theobserver/commentisfree/2021/may/09/how-good-are-we-at-predicting-pandemic.
Callaway, E. (2021). Mice with severe COVID symptoms could speed vaccine effort. Nature. https://doi.org/10.1038/d41586-021-01251-0
Geng, X., Katul, G. G., Gerges, F., Bou-Zeid, E., Nassif, H., & Boufadel, M. C. (2021). A kernel-modulated SIR model for Covid-19 contagious spread from county to continent. Proceedings of the National Academy of Sciences, 118(21). https://doi.org/10.1073/pnas.2023321118
ProMod3 uses the OpenMM library (Eastman et al.) to perform the computations and the CHARMM22/CMAP force field (Mackerell et al.) for parameterisation
Homology model built by SwissModel is minimized by OpenMM
Buitendijk, S., Ward, H., Shimshon, G., Sam, A. H., Sharma, D., & Harris, M. (2020). COVID-19: An opportunity to rethink global cooperation in higher education and research. BMJ Global Health, 5(7), e002790. https://doi.org/10.1136/bmjgh-2020-002790
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Maarten van Smeden on Twitter. (n.d.). Twitter. Retrieved 4 March 2021, from https://twitter.com/MaartenvSmeden/status/1328093246829064192
Covid One Year Ago. (2021, March 5). Modelling assumes that suppression measures can be sustained for a maximum of 3-4 months, so introducing early ’lockdown’-style measures to stop the disease is judged likely to lead only to a more deadly resurgence later on when they are lifted https://t.co/QRxgRj3jW3 https://t.co/pbqTAVGDfG [Tweet]. @YearCovid. https://twitter.com/YearCovid/status/1367778417437929472
Peter Sheridan Dodds. (2021, March 7). The map is not the territory. And the mapmakers are not the map. [Tweet]. @peterdodds. https://twitter.com/peterdodds/status/1368559285182099463
Cepelewicz, J. (n.d.). The Hard Lessons of Modeling the Coronavirus Pandemic. Quanta Magazine. Retrieved February 11, 2021, from https://www.quantamagazine.org/the-hard-lessons-of-modeling-the-coronavirus-pandemic-20210128/
Piotrowska, M. J., Sakowski, K., Karch, A., Tahir, H., Horn, J., Kretzschmar, M. E., & Mikolajczyk, R. T. (2020). Modelling pathogen spread in a healthcare network: Indirect patient movements. PLOS Computational Biology, 16(11), e1008442. https://doi.org/10.1371/journal.pcbi.1008442
We know the audience for such games is limited. In order for us to produce games up to our standards, we rely on a direct sales model. Our games are not designed for traditional distribution or retail channels. The vast majority of all copies produced will be sent to Kickstarter backers or to people who purchase games through our store. This means we can spend many more resources on the game's physical production without having to worry about retail viability.
Only the Starter Kit is available in this reboot. The Starter Kit is FREE, in order to distribute it as widely as possible. This goal of this Kickstarter campaign is to introduce Clash of Deck to the whole word and to bring a community together around the game. If the Kickstarter campaign succeeds, we will then have the necessary dynamic to publish additional paid content on a regular basis, to enrich the game with: stand-alone expansions, additional modules, alternative game modes..
Western News—Nearly 40,000 kids in the U.S. who lost a parent to COVID-19 need immediate support. (2021, April 5). Western News. https://news.westernu.ca/2021/04/covid-19-parent-loss/
delivering C2C parcels within short distances (metro) profitably seems like a difficult problem to solve
A point-to-point system connects a set of locations directly with all locations interacting with each other, i.e. a simple pickup up and drop off system
Nobody is unaware of Uber and Uber’s services. Hence, considering those services, any start-up entrepreneur, before thinking of starting a similar business, may have a doubt regarding the business model of Uber and revenue model of Uber
How is it that https://en.wikipedia.org/wiki/Type_theory links to https://en.wikipedia.org/wiki/Type_(model_theory) but the latter does not have any link to or mention of https://en.wikipedia.org/wiki/Type_theory
Neither mentions the relationship between them, but both of them should, since I expect that is a common question.
Model theory recognizes and is intimately concerned with a duality: it examines semantical elements (meaning and truth) by means of syntactical elements (formulas and proofs) of a corresponding language
Jones, M. I., Sirianni, A. D., & Fu, F. (2021). Polarization, Abstention, and the Median Voter Theorem. ArXiv:2103.12847 [Physics]. http://arxiv.org/abs/2103.12847
Nsoesie, E. O., Oladeji, O., Abah, A. S. A., & Ndeffo-Mbah, M. L. (2021). Forecasting influenza-like illness trends in Cameroon using Google Search Data. Scientific Reports, 11(1), 6713. https://doi.org/10.1038/s41598-021-85987-9
Machine learning models for diagnosing COVID-19 are not yet suitable for clinical use. (2021, March 15). University of Cambridge. https://www.cam.ac.uk/research/news/machine-learning-models-for-diagnosing-covid-19-are-not-yet-suitable-for-clinical-use
Oraby, T., Thampi, V., & Bauch, C. T. (2014). The influence of social norms on the dynamics of vaccinating behaviour for paediatric infectious diseases. Proceedings of the Royal Society B: Biological Sciences, 281(1780). https://doi.org/10.1098/rspb.2013.3172
Hong, I., Frank, M. R., Rahwan, I., Jung, W.-S., & Youn, H. (2020). The universal pathway to innovative urban economies. Science Advances, 6(34), eaba4934. https://doi.org/10.1126/sciadv.aba4934
Larremore, D. B., Wilder, B., Lester, E., Shehata, S., Burke, J. M., Hay, J. A., Tambe, M., Mina, M. J., & Parker, R. (2020). Test sensitivity is secondary to frequency and turnaround time for COVID-19 surveillance. MedRxiv, 2020.06.22.20136309. https://doi.org/10.1101/2020.06.22.20136309
Forecasting for COVID-19 has failed. (2020, June 14). International Institute of Forecasters. https://forecasters.org/blog/2020/06/14/forecasting-for-covid-19-has-failed/
COVID-19 Infections Tracker. (n.d.). COVID-19 Projections Using Machine Learning. Retrieved June 20, 2020, from https://covid19-projections.com/infections-tracker/
Silverman, J. D., Hupert, N., & Washburne, A. D. (2020). Using influenza surveillance networks to estimate state-specific prevalence of SARS-CoV-2 in the United States. Science Translational Medicine. https://doi.org/10.1126/scitranslmed.abc1126
Metcalf, C. J. E., Morris, D. H., & Park, S. W. (2020). Mathematical models to guide pandemic response. Science, 369(6502), 368–369. https://doi.org/10.1126/science.abd1668
Baker, C. M., Campbell, P. T., Chades, I., Dean, A. J., Hester, S. M., Holden, M. H., McCaw, J. M., McVernon, J., Moss, R., Shearer, F. M., & Possingham, H. P. (2020). From climate change to pandemics: Decision science can help scientists have impact. ArXiv:2007.13261 [Physics]. http://arxiv.org/abs/2007.13261
Dehning, J., Zierenberg, J., Spitzner, F. P., Wibral, M., Neto, J. P., Wilczek, M., & Priesemann, V. (2020). Inferring change points in the spread of COVID-19 reveals the effectiveness of interventions. Science. https://doi.org/10.1126/science.abb9789
Di Lauro, F., Berthouze, L., Dorey, M. D., Miller, J. C., & Kiss, I. Z. (2020). The impact of network properties and mixing on control measures and disease-induced herd immunity in epidemic models: A mean-field model perspective. ArXiv:2007.06975 [Physics, q-Bio]. http://arxiv.org/abs/2007.06975
Bertana, A., Chetverikov, A., Bergen, R. S. van, Ling, S., & Jehee, J. F. M. (2020). Dual strategies in human confidence judgments. BioRxiv, 2020.09.17.299743. https://doi.org/10.1101/2020.09.17.299743
Cheng, C., Barceló, J., Hartnett, A. S., Kubinec, R., & Messerschmidt, L. (2020). COVID-19 Government Response Event Dataset (CoronaNet v.1.0). Nature Human Behaviour, 1–13. https://doi.org/10.1038/s41562-020-0909-7
Rahnev, D. (2020). Confidence in the Real World. Trends in Cognitive Sciences. https://doi.org/10.1016/j.tics.2020.05.005
Kozlowski, Diego, Jennifer Dusdal, Jun Pang, and Andreas Zilian. ‘Semantic and Relational Spaces in Science of Science: Deep Learning Models for Article Vectorisation’. ArXiv:2011.02887 [Physics], 5 November 2020. http://arxiv.org/abs/2011.02887.
Holme, P. (2021). Fast and principled simulations of the SIR model on temporal networks. PLOS ONE, 16(2), e0246961. https://doi.org/10.1371/journal.pone.0246961
Cantwell, G. T., Kirkley, A., & Newman, M. E. J. (2020). The friendship paradox in real and model networks. ArXiv:2012.03991 [Physics]. http://arxiv.org/abs/2012.03991
Gupta, R. K., Marks, M., Samuels, T. H. A., Luintel, A., Rampling, T., Chowdhury, H., Quartagno, M., Nair, A., Lipman, M., Abubakar, I., Smeden, M. van, Wong, W. K., Williams, B., & Noursadeghi, M. (2020). Systematic evaluation and external validation of 22 prognostic models among hospitalised adults with COVID-19: An observational cohort study. MedRxiv, 2020.07.24.20149815. https://doi.org/10.1101/2020.07.24.20149815
we head straight into an additional terminus, or end event as it’s called in BPMN
no
Haug, N., Geyrhofer, L., Londei, A., Dervic, E., Desvars-Larrive, A., Loreto, V., Pinior, B., Thurner, S., & Klimek, P. (2020). Ranking the effectiveness of worldwide COVID-19 government interventions. Nature Human Behaviour, 4(12), 1303–1312. https://doi.org/10.1038/s41562-020-01009-0
McKenna, S. (n.d.). COVID Models Show How to Avoid Future Lockdowns. Scientific American. Retrieved 26 February 2021, from https://www.scientificamerican.com/article/covid-models-show-how-to-avoid-future-lockdowns/
Aletti, G., Crimaldi, I., & Saracco, F. (2020). A model for the Twitter sentiment curve. ArXiv:2011.05933 [Physics]. http://arxiv.org/abs/2011.05933
Smaldino, Paul E., and Cailin O’Connor. ‘Interdisciplinarity Can Aid the Spread of Better Methods Between Scientific Communities’. MetaArXiv, 5 November 2020. https://doi.org/10.31222/osf.io/cm5v3.
Wang, X., Sirianni, A. D., Tang, S., Zheng, Z., & Fu, F. (2020). Public Discourse and Social Network Echo Chambers Driven by Socio-Cognitive Biases. Physical Review X, 10(4), 041042. https://doi.org/10.1103/PhysRevX.10.041042
Ye, Y., Zhang, Q., Ruan, Z., Cao, Z., Xuan, Q., & Zeng, D. D. (2020). Effect of heterogeneous risk perception on information diffusion, behavior change, and disease transmission. Physical Review E, 102(4), 042314. https://doi.org/10.1103/PhysRevE.102.042314
Anderson, S. C., Edwards, A. M., Yerlanov, M., Mulberry, N., Stockdale, J. E., Iyaniwura, S. A., Falcao, R. C., Otterstatter, M. C., Irvine, M. A., Janjua, N. Z., Coombs, D., & Colijn, C. (2020). Quantifying the impact of COVID-19 control measures using a Bayesian model of physical distancing. PLOS Computational Biology, 16(12), e1008274. https://doi.org/10.1371/journal.pcbi.1008274
Stewart, A. J., McCarty, N., & Bryson, J. J. (2020). Polarization under rising inequality and economic decline. Science Advances, 6(50), eabd4201. https://doi.org/10.1126/sciadv.abd4201
Intuitively, you understand the flow just by looking at the BPMN diagram. And, heck, we haven’t even discussed BPMN or any terminology, yet!
Around 2 years ago I decided to end the experiment of “TRB PRO” as I felt I didn’t provide enough value to paying users. In the end, we had around 150 companies and individuals signed up, which was epic and a great funding source for more development.
We’re now relaunching PRO, but instead of a paid chat and (never existing) paid documentation, your team gets access to paid gems, our visual editor for workflows, and a commercial license.
We use a subset of BPMN for the visual language in the editor, but added our own set of restrictions and semantics to it.
Business Process Model and Notation (BPMN) is a standard for business process modeling that provides a graphical notation for specifying business processes in a Business Process Diagram (BPD),[3] based on a flowcharting technique very similar to activity diagrams from Unified Modeling Language (UML).
Hickok, A., Kureh, Y., Brooks, H. Z., Feng, M., & Porter, M. A. (2021). A Bounded-Confidence Model of Opinion Dynamics on Hypergraphs. ArXiv:2102.06825 [Nlin, Physics:Physics]. http://arxiv.org/abs/2102.06825
Compared to existing Ruby desktop frameworks, such as Shoes, Bowline's strengths are its adherence to MVC and use of HTML/JavaScript.
As with other software patterns, MVC expresses the "core of the solution" to a problem while allowing it to be adapted for each system.
Aminpour, P., Gray, S. A., Singer, A., Scyphers, S. B., Jetter, A. J., Jordan, R., Murphy, R., & Grabowski, J. H. (2021). The diversity bonus in pooling local knowledge about complex problems. Proceedings of the National Academy of Sciences, 118(5). https://doi.org/10.1073/pnas.2016887118
Tepper, S., & Neil Lewis, J. (2021). When the Going Gets Tough, How Do We Perceive the Future? PsyArXiv. https://doi.org/10.31234/osf.io/pkaxn
Study shows vaccine nationalism could cost rich countries US$4.5 trillion. (2021, January 25). ICC - International Chamber of Commerce. https://iccwbo.org/media-wall/news-speeches/study-shows-vaccine-nationalism-could-cost-rich-countries-us4-5-trillion/
Group, B. M. J. P. (2021). Update to living systematic review on prediction models for diagnosis and prognosis of covid-19. BMJ, 372, n236. https://doi.org/10.1136/bmj.n236
Not to mention 80% of our sales are laptops and desktops running, you guessed it, a Linux desktop. So, unlike Red Hat and Canonical, we live or die based on how good that experience is.
The background-origin CSS property sets the background's origin: from the border start, inside the border, or inside the padding.
Parag. K. V., Donnelly. C. A., (2020) Using information theory to optimise epidemic models for real-time prediction and estimation. PLOS. Retrieved from https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007990
Abel. M., Brown. W., (2020) Prosocial Behavior in the Time of COVID-19: The Effect of Private and Public Role Models. Institute of labor and economics. Retrieved from:https://covid-19.iza.org/publications/dp13207/
Donsimoni. J. R., Glawion. R., Plachter. B., Walde. K., (2020). Projecting the Spread of COVID-19 for Germany. Institute of labor economics. Retrieved from: https://covid-19.iza.org/publications/dp13094/
INTERACTION OF GROUND WATER AND STREAMS
gambar model yang sederhana. akan bagus kalau kita dapat menggambarkan sendiri (walaupun hanya dengan tangan) interaksi yang sama di daerah kita.
Eyal describes the theory called The Fogg Behavior Model which states that for a behavior (B) to occur, three things must be present at the same time: motivation (M), ability (A), and a trigger (T). More succinctly, B = MAT.
Fogg Behavior Model says that for a Behavior (B) to occur 3 things have to be present at the same time:
B = MAT
Better community building: At the moment, MDN content edits are published instantly, and then reverted if they are not suitable. This is really bad for community relations. With a PR model, we can review edits and provide feedback, actually having conversations with contributors, building relationships with them, and helping them learn.
Better contribution workflow: We will be using GitHub’s contribution tools and features, essentially moving MDN from a Wiki model to a pull request (PR) model. This is so much better for contribution, allowing for intelligent linting, mass edits, and inclusion of MDN docs in whatever workflows you want to add it to (you can edit MDN source files directly in your favorite code editor).
Rocca, R., & Yarkoni, T. (2020). Putting psychology to the test: Rethinking model evaluation through benchmarking and prediction. PsyArXiv. https://doi.org/10.31234/osf.io/e437b
There's a huge area of seemingly obvious user-centric products that don't exist simply because there isn't a working business model to support it.
CSS Object Model (CSSOM)
Soderberg, C. K., Errington, T., Schiavone, S. R., Bottesini, J. G., Thorn, F. S., Vazire, S., Esterling, K. M., & Nosek, B. A. (2020). Research Quality of Registered Reports Compared to the Traditional Publishing Model. MetaArXiv. https://doi.org/10.31222/osf.io/7x9vy
Linear mixed models are an extension of simple linear models to allow both fixed and random effects, and are particularly used when there is non independence in the data, such as arises from a hierarchical structure
We love dbt because of the values it embodies. Individual transformations are SQL SELECT statements, without side effects. Transformations are explicitly connected into a graph. And support for testing is first-class. dbt is hugely enabling for an important class of users, adapting software engineering principles to a slightly different domain with great ergonomics. For users who already speak SQL, dbt’s tooling is unparalleled.
when using [[dbt]] the [[transformations]] are [[SQL statements]] - already something that our team knows
We then estimate the relative weight each touch played in leading to a conversion. This estimation is done by allocating “points” to touches: each conversion is worth exactly one point, and that point is divvied up between the customer’s touches. There are four main ways to divvy up this point:First touch: Attribute the entire conversion to the first touchLast touch: Attribute the entire conversion to the last touchForty-twenty-forty: Attribute 40% (0.4 points) of the attribution to the first touch, 40% to the last touch, and divide the remaining 20% between all touches in betweenLinear: Divide the point equally among all touches
[[positional attribution]] works by identifying the touch points in the lifecycle, and dividing up the points across those touches.
There are four main ways to divvy up this pointing
[[question]] What are the four main ways to divvy up positional attribution]]
Once you have pageviews in your warehouse, you’ll need to do two thingsSessionization: Aggregate these pageviews into sessions (or “sessionization”) writing logic to identify gaps of 30 minutes or more.User stitching: If a user first visits your site without any identifying information (typically a `customer_id` or `email`), and then converts at a later date, their previous (anonymous) sessions should be updated to include their information. Your web tracking system should have a way to link these sessions together.This modeling is pretty complex, especially for companies with thousands of pageviews a day (thank goodness for incremental models 🙌). Fortunately, some very smart coworkers have written packages to do the heavy lifting for you, whether your page views are tracked with Snowplow, Segment or Heap. Leverage their work by installing the right package to transform the data for you.
[[1. Gather your required data sources]] - once we have data, we need to do two things [[sessionization]] - the aggregation of pageviews / etc into a session
and [[user stitching]] - when we have a user without any identifying information, and then converts - kind of like the anonymous users / signups - and trying to tie them back to a source
1. Gather your required data sourcesSessions:Required dbt techniques: packagesWe want to use a table that represents every time a customer interacts with our brand. For ecommerce companies, the closest thing we can get to for this is sessions. (If you’re instead working for a B2B organization, you should consider using a table of interactions between your sales team and a potential customer from your CRM).Sessions are discrete periods of activity by a user on a website. The industry standard is to define a session as a series of activities followed by a 30-minute window without any activity.
[[1. Gather your required data sources]]
How to build an attribution model
[[How to build an attribution model]]
The attribution data modelIn reality, it’s impossible to know exactly why someone converted to being a customer. The best thing that we can do as analysts, is provide a pretty good guess. In order to do that, we’re going to use an approach called positional attribution. This means, essentially, that we’re going to weight the importance of various touches (customer interactions with a brand) based on their position (the order they occur in within the customer’s lifetime).To do this, we’re going to build a table that represents every “touch” that someone had before becoming a customer, and the channel that led to that touch.
One of the goals of an [[attribution data model]] is to understand why someone [[converted]] to being a customer. This is impossible to do accurately, but this is where analysis comes in.
There are some [[approaches to attribution]], one of those is [[positional attribution]]
[[positional attribution]] is that we are weighting the importance of touch points - or customer interactions, based on their position within the customer lifetime.
transparent attribution model. You’re not relying on vendor logic. If your sales team feels like your attribution is off, show them dbt docs, walk them through the logic of your model, and make modifications with a single line of SQL
[[transparent attribution model]]
The most flexible attribution model. You own the business logic and you can extend it however you want, and change it easily when you business changes
[[flexible attribution model]]
hat’s it. Really! By writing SQL on top of raw data you get: The cheapest attribution model. This playbook assumes you’re operating within a modern data stack , so you already have the infrastructure that you need in place: You’re collecting events data with a tool like Snowplow or Segment (though Segment might get a little pricey) You’re extracting data from ad platforms using Stitch or Fivetran You’re loading data into a modern, cloud data warehouse like Snowflake, BigQuery, or Redshift And you’re using dbt so your analysts can model data in SQL
[[cheapest attribution model]]
So what do you actually need to build an attribution model?Raw data in your warehouse that represents customer interactions with your brand. For ecommerce companies, this is website visits. For B2B customers, it might be conversations with sales teams.SQL
to build an [[attribution model]] we need the raw data - this raw data should capture the [[customer interactions]], and in our case - also partner interactions, or people working with the partner?
This is addressing a security issue; and the associated threat model is "as an attacker, I know that you are going to do FROM ubuntu and then RUN apt-get update in your build, so I'm going to trick you into pulling an image that _pretents_ to be the result of ubuntu + apt-get update so that next time you build, you will end up using my fake image as a cache, instead of the legit one." With that in mind, we can start thinking about an alternate solution that doesn't compromise security.
Wunderling, N., Krönke, J., Wohlfarth, V., Kohler, J., Heitzig, J., Staal, A., Willner, S., Winkelmann, R., & Donges, J. F. (2020). Modelling nonlinear dynamics of interacting tipping elements on complex networks: The PyCascades package. ArXiv:2011.02031 [Nlin, Physics:Physics]. http://arxiv.org/abs/2011.02031
ORWG Virtual Meeting 08/09/2020 https://www.youtube.com/playlist?list=PLOA0aRJ90NxvXtMt5Si5ukmR9LYfvDueB (n.d.)
Modules from the following layer can require anything from all the previous layers, but not vice versa.
In order to inform the development and implementation of effective online learning environments, this study was designed to explore both instructors' and students' online learning experiences while enrolled in various online courses. The study investigated what appeared to both support and hinder participants' online teaching and learning experiences.
The authors discuss the issue of community and engagement in online graduate programs. They carried out a small case study and used a Cognitive Apprenticeship Model to examine a successful program in Higher Education. They found that students feel too many online classes are just reading and writing, regurgitating rather than applying, and lack sufficient connection with the instructor and with other students, They recommend some strategies to fix that, but admit that more work is needed. 9/10
The educator’s role in self-directed learning
Fostering self-directed learning through strategy is discussed by Bailey et al. (2019) in chapter 1 of “Self-Directed Learning for the 21st Century: Implications for Higher Education.” The authors review the changing role of the educator and the learner based on respective self-directed teaching strategies (problem-based learning, cooperative learning, process-oriented learning) and the learner’s propensity for self-directed learning. In addition to providing principles to promote self-directed learning, the Grow and Borich models for implementing said learning were briefly reviewed. 8/10
Cognitive Presence “is the extent to which learners are able to construct and confirm meaning through sustained reflection and discourse” (Community of Inquiry, n.d, para. 5). Video is often used as a unidirectional medium with information flowing from the expert or instructor to the learner. To move from transmission of content to construction of knowledge, tools such as Voice Thread (VoiceThread, 2016) support asynchronous conversation in a multimedia format.
The author, Kendra Grant, is the Director of Professional Development and Learning for Quillsoft in Toronto Canada. Grant helps business succeed in education design and support. In this article Grant discusses how quickly the learning environment has changed through technological development. Grant explores the RAT Model, which guides instructors in the "use of technology to help transform instructional practice." Grant then examines the Community of Inquiry model, which seeks to create meaningful instruction through social, cognitive and teaching presence. Grant concludes by providing general principles for creating a positive video presence.
Rating: 8/10
virtual-dom exposes a set of objects designed for representing DOM nodes. A "Document Object Model Model" might seem like a strange term, but it is exactly that. It's a native JavaScript tree structure that represents a native DOM node tree.
Grimm, V., Johnston, A. S. A., Thulke, H.-H., Forbes, V. E., & Thorbek, P. (2020). Three questions to ask before using model outputs for decision support. Nature Communications, 11(1), 4959. https://doi.org/10.1038/s41467-020-17785-2
Sia, S. F., Yan, L.-M., Chin, A. W. H., Fung, K., Choy, K.-T., Wong, A. Y. L., Kaewpreedee, P., Perera, R. A. P. M., Poon, L. L. M., Nicholls, J. M., Peiris, M., & Yen, H.-L. (2020). Pathogenesis and transmission of SARS-CoV-2 in golden hamsters. Nature, 583(7818), 834–838. https://doi.org/10.1038/s41586-020-2342-5
Karatayev, Vadim A., Madhur Anand, and Chris T. Bauch. ‘Local Lockdowns Outperform Global Lockdown on the Far Side of the COVID-19 Epidemic Curve’. Proceedings of the National Academy of Sciences 117, no. 39 (29 September 2020): 24575–80. https://doi.org/10.1073/pnas.2014385117.
Kaplan, Edward H, Dennis Wang, Mike Wang, Amyn A Malik, Alessandro Zulli, and Jordan H Peccia. ‘Aligning SARS-CoV-2 Indicators via an Epidemic Model: Application to Hospital Admissions and RNA Detection in Sewage Sludge’. Preprint. Infectious Diseases (except HIV/AIDS), 29 June 2020. https://doi.org/10.1101/2020.06.27.20141739.
BPMN Viewer and Editor Use bpmn-js to display BPMN 2.0 diagrams on your website. Embed it as a BPMN 2.0 web modeler into your applications and customize it to suit your needs.
Business Process Model and Notation (BPMN) is a graphical representation for specifying business processes in a business process model.
Kubinec, Robert & Carvalho, Luiz & Barceló, Joan & Cheng, Cindy & Hartnett, Allison & Messerschmidt, Luca & Duba, Derek & Cottrell, Matthew Sean, 2020. "Partisanship and the Spread of COVID-19 in the United States," SocArXiv jp4wk, Center for Open Science. Retrieved from: https://ideas.repec.org/p/osf/socarx/jp4wk.html
Team, I. C.-19 F., & Hay, S. I. (2020). COVID-19 scenarios for the United States. MedRxiv, 2020.07.12.20151191. https://doi.org/10.1101/2020.07.12.20151191
Yang, Scott Cheng-Hsin, Chirag Rank, Jake Alden Whritner, Olfa Nasraoui, and Patrick Shafto. ‘Unifying Recommendation and Active Learning for Information Filtering and Recommender Systems’. Preprint. PsyArXiv, 25 August 2020. https://doi.org/10.31234/osf.io/jqa83.
mongoose.model
mongoose.model()
When you call mongoose.model() on a schema, Mongoose compiles a model for you. The first argument is the singular name of the collection your model is for. Mongoose automatically looks for the plural, lowercased version of your model name. https://mongoosejs.com/docs/models.html#compiling
Covid-19 has decimated independent U.S. primary care practices—How should policymakers and payers respond? (2020, July 2). The BMJ. https://blogs.bmj.com/bmj/2020/07/02/covid-19-has-decimated-independent-u-s-primary-care-practices-how-should-policymakers-and-payers-respond/
Candido, D. S., Claro, I. M., Jesus, J. G. de, Souza, W. M., Moreira, F. R. R., Dellicour, S., Mellan, T. A., Plessis, L. du, Pereira, R. H. M., Sales, F. C. S., Manuli, E. R., Thézé, J., Almeida, L., Menezes, M. T., Voloch, C. M., Fumagalli, M. J., Coletti, T. M., Silva, C. A. M. da, Ramundo, M. S., … Faria, N. R. (2020). Evolution and epidemic spread of SARS-CoV-2 in Brazil. Science. https://doi.org/10.1126/science.abd2161
Eichenbaum, M. S., Rebelo, S., & Trabandt, M. (2020). The Macroeconomics of Epidemics (Working Paper No. 26882; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w26882
Bertuzzo, E., Mari, L., Pasetto, D., Miccoli, S., Casagrandi, R., Gatto, M., & Rinaldo, A. (2020). The geography of COVID-19 spread in Italy and implications for the relaxation of confinement measures. Nature Communications, 11(1), 4264. https://doi.org/10.1038/s41467-020-18050-2
The RAT model sees software development as an off-line program-construction activity composed of these parts: defining, decomposing, estimating, implementing, assembling, and finishing
This is what can lead to the 'there is only version 1.0' problem - and improvements / iterations fall to the sidelines.
This can have a number of consequences
Kreye, J., Reincke, S. M., Kornau, H.-C., Sánchez-Sendin, E., Corman, V. M., Liu, H., Yuan, M., Wu, N. C., Zhu, X., Lee, C.-C. D., Trimpert, J., Höltje, M., Dietert, K., Stöffler, L., Wardenburg, N. von, Hoof, S. van, Homeyer, M. A., Hoffmann, J., Abdelgawad, A., … Prüss, H. (2020). A SARS-CoV-2 neutralizing antibody protects from lung pathology in a COVID-19 hamster model. BioRxiv, 2020.08.15.252320. https://doi.org/10.1101/2020.08.15.252320
Shi, W., Wang, L., & Qin, J. (2020). Extracting user influence from ratings and trust for rating prediction in recommendations. Scientific Reports, 10(1), 13592. https://doi.org/10.1038/s41598-020-70350-1
Engelhardt, R., Hendricks, V. F., & Stærk-Østergaard, J. (2020). The Wisdom and Persuadability of Threads. ArXiv:2008.05203 [Physics]. http://arxiv.org/abs/2008.05203
Coibion, O., Gorodnichenko, Y., & Weber, M. (2020). Does Policy Communication During Covid Work? (Working Paper No. 27384; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27384
Atkeson, A. (2020). What Will Be the Economic Impact of COVID-19 in the US? Rough Estimates of Disease Scenarios (Working Paper No. 26867; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w26867
Allcott, H., Boxell, L., Conway, J. C., Gentzkow, M., Thaler, M., & Yang, D. Y. (2020). Polarization and Public Health: Partisan Differences in Social Distancing during the Coronavirus Pandemic (Working Paper No. 26946; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w26946
Chaudhuri, S., Lo, A. W., Xiao, D., & Xu, Q. (2020). Bayesian Adaptive Clinical Trials for Anti‐Infective Therapeutics during Epidemic Outbreaks (Working Paper No. 27175; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27175
Avery, C., Bossert, W., Clark, A., Ellison, G., & Ellison, S. F. (2020). Policy Implications of Models of the Spread of Coronavirus: Perspectives and Opportunities for Economists (Working Paper No. 27007; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27007
Alvarez, F. E., Argente, D., & Lippi, F. (2020). A Simple Planning Problem for COVID-19 Lockdown (Working Paper No. 26981; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w26981
Barnett, M., Buchak, G., & Yannelis, C. (2020). Epidemic Responses Under Uncertainty (Working Paper No. 27289; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27289
Razin, A., Sadka, E., & Schwemmer, A. H. (2020). DEglobalizaion and Social Safety Nets in Post-Covid-19 Era: Textbook Macroeconomic Analysis (Working Paper No. 27239; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27239
Alfaro, L., Faia, E., Lamersdorf, N., & Saidi, F. (2020). Social Interactions in Pandemics: Fear, Altruism, and Reciprocity (Working Paper No. 27134; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27134
Eichenbaum, M. S., Rebelo, S., & Trabandt, M. (2020). Epidemics in the Neoclassical and New Keynesian Models (Working Paper No. 27430; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27430
Jordà, Ò., Singh, S. R., & Taylor, A. M. (2020). Longer-run Economic Consequences of Pandemics (Working Paper No. 26934; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w26934
Berger, D. W., Herkenhoff, K. F., & Mongey, S. (2020). An SEIR Infectious Disease Model with Testing and Conditional Quarantine (Working Paper No. 26901; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w26901
Gormsen, N. J., & Koijen, R. S. J. (2020). Coronavirus: Impact on Stock Prices and Growth Expectations (Working Paper No. 27387; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27387
Fujita, Shigeru, Giuseppe Moscarini, and Fabien Postel-Vinay. ‘Measuring Employer-to-Employer Reallocation’. Working Paper. Working Paper Series. National Bureau of Economic Research, July 2020. https://doi.org/10.3386/w27525.
Chari, Varadarajan V, Rishabh Kirpalani, and Christopher Phelan. ‘The Hammer and the Scalpel: On the Economics of Indiscriminate versus Targeted Isolation Policies during Pandemics’. Working Paper. Working Paper Series. National Bureau of Economic Research, May 2020. https://doi.org/10.3386/w27232.
Stock, James H. ‘Data Gaps and the Policy Response to the Novel Coronavirus’. Working Paper. Working Paper Series. National Bureau of Economic Research, March 2020. https://doi.org/10.3386/w26902.
Burke, Marshall, Anne Driscoll, Jenny Xue, Sam Heft-Neal, Jennifer Burney, and Michael Wara. ‘The Changing Risk and Burden of Wildfire in the US’. Working Paper. Working Paper Series. National Bureau of Economic Research, June 2020. https://doi.org/10.3386/w27423.
Céspedes, L. F., Chang, R., & Velasco, A. (2020). The Macroeconomics of a Pandemic: A Minimalist Model (Working Paper No. 27228; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27228
Malani, A., Soman, S., Asher, S., Novosad, P., Imbert, C., Tandel, V., Agarwal, A., Alomar, A., Sarker, A., Shah, D., Shen, D., Gruber, J., Sachdeva, S., Kaiser, D., & Bettencourt, L. M. A. (2020). Adaptive Control of COVID-19 Outbreaks in India: Local, Gradual, and Trigger-based Exit Paths from Lockdown (Working Paper No. 27532; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27532
Bethune, Z. A., & Korinek, A. (2020). Covid-19 Infection Externalities: Trading Off Lives vs. Livelihoods (Working Paper No. 27009; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27009
Galbadage, T., Peterson, B. M., Wang, D. C., Wang, J. S., & Gunasekera, R. S. (2020). Biopsychosocial and Spiritual Implications of Patients with COVID-19 Dying in Isolation [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/7um3x
Quinn, A. E., Trachtenberg, A. J., McBrien, K. A., Ogundeji, Y., Souri, S., Manns, L., Rennert-May, E., Ronksley, P., Au, F., Arora, N., Hemmelgarn, B., Tonelli, M., & Manns, B. J. (2020). Impact of payment model on the behaviour of specialist physicians: A systematic review. Health Policy, 124(4), 345–358. https://doi.org/10.1016/j.healthpol.2020.02.007
Acemoglu, D., Chernozhukov, V., Werning, I., & Whinston, M. D. (2020). Optimal Targeted Lockdowns in a Multi-Group SIR Model (Working Paper No. 27102; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27102
Bianchi, F., Faccini, R., & Melosi, L. (2020). Monetary and Fiscal Policies in Times of Large Debt: Unity is Strength (Working Paper No. 27112; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27112
Kominers, S. D., Pathak, P. A., Sönmez, T., & Ünver, M. U. (2020). Paying It Backward and Forward: Expanding Access to Convalescent Plasma Therapy Through Market Design (Working Paper No. 27143; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27143
Baqaee, D., Farhi, E., Mina, M. J., & Stock, J. H. (2020). Reopening Scenarios (Working Paper No. 27244; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27244
Holme, P. (2020). Fast and principled simulations of the SIR model on temporal networks. ArXiv:2007.14386 [Physics, q-Bio]. http://arxiv.org/abs/2007.14386
Jinjarak, Y., Ahmed, R., Nair-Desai, S., Xin, W., & Aizenman, J. (2020). Pandemic Shocks and Fiscal-Monetary Policies in the Eurozone: COVID-19 Dominance During January - June 2020 (Working Paper No. 27451; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27451
Aksoy, C. G., Eichengreen, B., & Saka, O. (2020). The Political Scar of Epidemics (Working Paper No. 27401; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27401
Caballero, R. J., & Simsek, A. (2020). A Model of Asset Price Spirals and Aggregate Demand Amplification of a (Working Paper No. 27044; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27044
Baqaee, D., & Farhi, E. (2020). Nonlinear Production Networks with an Application to the Covid-19 Crisis (Working Paper No. 27281; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27281
Gupta, S., Montenovo, L., Nguyen, T. D., Rojas, F. L., Schmutte, I. M., Simon, K. I., Weinberg, B. A., & Wing, C. (2020). Effects of Social Distancing Policy on Labor Market Outcomes (Working Paper No. 27280; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27280
Another Ruby gem, Spira, allows graph data to be used as model objects
Al-Ubaydli, O., Lee, M. S., List, J. A., Mackevicius, C. L., & Suskind, D. (undefined/ed). How can experiments play a greater role in public policy? Twelve proposals from an economic model of scaling. Behavioural Public Policy, 1–48. https://doi.org/10.1017/bpp.2020.17
Atkeson, A. (2020). How Deadly Is COVID-19? Understanding The Difficulties With Estimation Of Its Fatality Rate (Working Paper No. 26965; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w26965
Fernández-Villaverde, J., & Jones, C. I. (2020). Estimating and Simulating a SIRD Model of COVID-19 for Many Countries, States, and Cities (Working Paper No. 27128; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27128
Fajgelbaum, P., Khandelwal, A., Kim, W., Mantovani, C., & Schaal, E. (2020). Optimal Lockdown in a Commuting Network (Working Paper No. 27441; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27441
Shorenstein APARC. (2020, June 10). Rebooting Business After COVID-19: A View From China. https://www.youtube.com/watch?v=5DDqlzp_gKc
At the substitution level, you are substituting a cup of coffee that we could make at home or school with a cup of coffee from Starbucks. It’s still coffee: there’s no real change.
Love this example with one of my favorite things: coffee! Having these examples are very helpful to me, this article not only provides examples, though, it explains why they are examples of each
The SAMR model allows you the opportunity to evaluate why you are using a specific technology, design tasks that enable higher-order thinking skills, and engage students in rich learning experiences.
Clearly stated purpose of the SAMR model!
The SAMR Ladder:Questions and Transitions
Helpful resource here
Webster, G. D., Mahar, E., & Wongsomboon, V. (2020). American Psychology Is Becoming More International, But Too Slowly: Comment on Thalmayer et al. (2020). https://doi.org/10.31234/osf.io/wqmer Ame
Brooks, H. Z., Kanjanasaratool, U., Kureh, Y. H., & Porter, M. A. (2020). Disease Detectives: Using Mathematics to Forecast the Spread of Infectious Diseases [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/mvn9z
Block, P., Hoffman, M., Raabe, I. J., Dowd, J. B., Rahal, C., Kashyap, R., & Mills, M. C. (2020). Social network-based distancing strategies to flatten the COVID-19 curve in a post-lockdown world. Nature Human Behaviour, 4(6), 588–596. https://doi.org/10.1038/s41562-020-0898-6
Oxford leads development of risk prediction model for more tailored COVID-19 shielding advice | University of Oxford. (n.d.). Retrieved 23 June 2020, from http://www.ox.ac.uk/news/2020-06-22-oxford-leads-development-risk-prediction-model-more-tailored-covid-19-shielding
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Rahman, M., Ali, G. G. M. N., Li, X. J., Paul, K. C., & Chong, P. H. J. (2020). Twitter and Census Data Analytics to Explore Socioeconomic Factors for Post-COVID-19 Reopening Sentiment [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/fz4ry
Altmann, E. G. (2020). Spatial interactions in urban scaling laws. ArXiv:2006.14140 [Physics]. http://arxiv.org/abs/2006.14140
Zhang, L., & Peixoto, T. P. (2020). Statistical inference of assortative community structures. ArXiv:2006.14493 [Cond-Mat, Physics:Physics, Stat]. http://arxiv.org/abs/2006.14493
Ben-David, S. (2018). Clustering—What Both Theoreticians and Practitioners are Doing Wrong. ArXiv:1805.08838 [Cs, Stat]. http://arxiv.org/abs/1805.08838
Sharma, N., Uttrani, S., & Dutt, V. (2020, June 19). Modeling the Absence of Framing Effect in an Experience-based Covid-19 Disease Problem. 18th Annual Meeting of the International Conference on Cognitive Modelling. https://www.researchgate.net/publication/342313460_Modeling_the_Absence_of_Framing_Effect_in_an_Experience-based_Covid-19_Disease_Problem
Barbaro, N., Richardson, G. B., Nedelec, J. L., & Liu, H. (2020). Assessing Effects of Life History Antecedents on Age at Menarche and Sexual Debut Using a Genetically Informative Design [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/xqfg8
Gibson Miller, J., Hartman, T. K., Levita, L., Martinez, A. P., Mason, L., McBride, O., … Bentall, R. (2020, April 20). Capability, opportunity and motivation to enact hygienic practices in the early stages of the COVID-19 outbreak in the UK. https://doi.org/10.31234/osf.io/typqv
Kliff, Sarah. ‘How’s the Economy Doing? Watch the Dentists’. The New York Times, 10 June 2020, sec. The Upshot. https://www.nytimes.com/2020/06/10/upshot/dentists-coronavirus-economic-indicator.html.
Horn, S. R., Weston, S. J., & Fisher, P. (2020). Identifying causal role of COVID-19 in immunopsychiatry models [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/w4d5u
Del Giudice, M. (2020). All About AIC [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/7hmgz
de Arruda, G. F., Méndez-Bermúdez, J. A., Rodrigues, F. A., & Moreno, Y. (2020). Universality of eigenvector delocalization and the nature of the SIS phase transition in multiplex networks. ArXiv:2005.08074 [Cond-Mat, Physics:Physics]. http://arxiv.org/abs/2005.08074