rhetoric: Schwarz uses a statement of transition to contrast the old education model (rote memorization of commands) with a new required model (critical examination).

inference: This supports the argument that education and labor must start to pivot away from the "Generalist" process-oriented tasks. If the machine assistants handle the 'How' (the commands and functions), then the human must focus more on the 'Why' and the 'what does it mean (understanding/wisdom)'. This helps to validate the work of the assistants and helps to make it useful and valuable in the real world.

rhetoric: author presents a prescriptive claim that AI needs humans with competent knowledge (in this case, statistics) to create prompts and ensure that the AI does not leave out any steps of the analysis. He positions domain knowledge not as a tool for using AI for statistical analysis, but a prerequisite for management of the AI and auditing the output.

inference: In addition to policing and correcting the AI outputs, the deep domain knowledge is what allows the AI to do complex data analysis without mistakes, hallucinated results, or mathematically false outcomes. This is basically the job description of a human with "Augmented Human Wisdom". The human's value is no longer in doing math, but in possessing the vertical expertise (flesh/wisdom) to know exact what math needs to be done and ultimately auditing the assistant machine's work.

rhetoric: Schwarz employs cause-and-effect reasoning here based on empirical testing. He links a specific technical failure (not checking assumptions) to a definitive unwanted outcome (a false result).

inference: the "Data Analyst" function of ChatGPT hallucinated a result during the use of it's core function! This is the best evidence so far of the 'Crisis of Truth' and the dangers of the 'Headless Automatons' in my essay. If a generalist with no deep knowledge uses AI, they are at great risk of blindly accepting mathematically false conclusions. Synthetic syntax without competent human validation is a liability.

rhetoric: author uses comparative, objective statement (logos) to establish the main boundary of the technology's capability/capacity -- it excels at technical generation (things like coding) but fails at standard procedures (methodological adherence to SOPs).

inference: the proves the 'Raising the Floor' concept. AI completely automates the entry-level syntax (the "Word"), meaning that the Generalist coder is obsolete! However, because it fails at standard procedures, it requires a human architect to guide it to outputs that are valuable in the real world.

rhetoric: concluding statement. this positions the LLM as an 'enhancement' to physical human limitation, rather than a replacement of the human subject.

inference: This helps to validate the 'Augmented Human Wisdom' model. The future of AI is NOT replacing humans, but AI acting as a high-powered syntax engine that is strictly guided by human needs and human intent. The AI does not have 'agency', as it is a software tool that helps the human to execute their visions.

rhetoric: authors explain an unintended consequence of using the AI tool: it scales the errors or the emptiness of the human prompt.

inference: this is an excellent metaphor for the 'manager fallacy'. If the human user in incompetent (or provides empty or incomplete input), the AI does not magically create wisdom -- it just amplifies the user's incompetence in a a highly articulate synthetic thought.

rhetoric: researchers use a specific, clinical observation of an error to demonstrate the model's inability to comprehend logical reality despite the human relaying a perfectly structured sentence.

inference: this shows that AI is amoral and lacks the lived experience necessary to make logical judgments that work in the real world. It can format a sentence beautifully, but it does not/will not always understand that a single human cannot be two ages at once. This is why it is very important/necessary for the "flesh" to text the output against reality

rhetoric: authors provide a definition of their creation (Aphasia-GPT) to describe it's mechanism: taking a fragmented input and expanding it into a fully structured, complete output.

inference: this is the embodiment of Harari's primary metanym of the word v flesh (syntax v human). in this example, Aphasia-GPT provides the words (syntax) to the fleshy human that struggles with those words, while also relying on the human to spark the intent of the communication. The human is using AI to communicate with words, because the words are very difficult for the human.

rhetoric: counter-argument from the political left, that highlights the flaws and potential inequities for distributing via blanket model like UBI

inference: This is a great steelman addition to my argument against UBI -- this actually shows that both conservatives (fearing inflation) and progressives (fearing inequity) recognize the flaws of UBI. I can use this in my essay to help dismiss it as a viable solution for the displaced generalists.

rhetoric: this is an appeal to macro econ theory (logos), citing 'expert analysts' that warn of the mathematical consequences of extra 'unearned' capital (without being tied to production) in the market.

inference: this is how I feel about the concept -- receiving money without earning it could lead to major issues down the road, especially if a large majority of the people decide to stop working in jobs that help to add value to society. Printing $$$ w/o corresponding human production (because the machine is doing the work) is a trap that dangerously increases the risk if price inflation (hyperinflation) and income stagnation, because it removes the motivation to continue adding value, and increases the incentive to essentially do nothing ('eat, drink, be merry'). This is one of the core arguments for my thesis, that humans are abdicating their agency, or at least at a very real danger of it, which leads to an infinite loop of "Workslop" ("Work slop" Medici)

rhetoric: summary of the counter-argument from the conservative side, using cause and effect reasoning/logic (that UBI causes loss of incentive)

inference: this is a major risk, and since these payments would be delivered to entire large economy, this could potentially cause the entire structure to collapse, because the productive class can very easily and quickly shrink, with a massive bill that the taxpayers (productive class) would need to cover.

rhetoric: authors/editors use an objective definition of Universal Basic Income

inference: I have seen this a lot over the last few years. Andrew Yang mentioned it for his presidential campaigns, and gave the opinion a lot of credibility. This helps to explain or establish the concept in my paper, which I can present as a false solution that harms everyone involved, including the taxpayers. It is proposed as the "solution" to AI disrupting the workplace today!

AI Slop generated by your own hands changes he way people view you at work!

$186/per employee/per month!

10 employees = ($22,320) 25 employees = ($55,800) 50 employees = ($111,600) 100 employees = ($223,200) 250 employees = ($558,000) 500 employees = ($1,116,000) 1000 employees = ($2,232,000)

this is another great quote, which helps to establish how orgs treat younger generations, and how they tend to overtrust their understanding of AI.

Good stat from a survey of 1100 managers at resume.com. 70% of managers say that their employees have submitted AI generated errors inside their work in the last year.

from reporting managers, 58% of them said that employees were submitting work that contained factual inaccuracies in the work that was generated by AI, and that fewer of them reported that AI failed to account for "critical contextual factors", implying that the writing was generic and not directly applicable to the context that the writing was written in. Other issues were: low quality content, poor recommendations and inappropriate messaging.

Extra time and money spent to repair errors made by AI but not caught by the human in the middle. 59% is almost 2/3 (closer to 3/5) needed to correct or redo the work created by AI without a human auditing it. 53% claim extra work is needed to repair the AI mistakes, and 45% also needed to bring in a (perhaps more senior) co-worker to help fix the mistake. I can imagine workers needing to work on a mistake the hits production code, and all of the thousands (or more) mistakes that would need to be later repaired and rolled back. very expensive and costly.

missed deadlines are tough, but damaging client relationships is a sin!

great stats for the cost of using AI without human auditing.

while this affect all generations, this is clearly a young people problem! This problem is here to stay

great quote. directly mentions my concept of requiring human judgement, and how not having a human in the loop can make work move faster, but can also lead to very costly mistakes.

another great quote -- ties into the abdicating human agency to a robot, and the full quote even illustrates the dangers of doing so.

great quote. validates my thesis that AI cannot replace the human, and the human needs to be the brains for the AI worker. This also directly relates to my concept that humans have a tendency to abdicate their agency to AI, as if it is superior in human logic, context, emotional intelligence and critical analysis within the context of the human world.

'workslop' is creeping into businesses. This section mentions AI-generated content, slide decks, and even lengthy reports or 'random code' being passed off as polished work by employees!

This is the empirical proof that ungrammatical, casual, non-standard text causes an immediate, sharp spike in cognitive effort, validating the argument for standardization to lower cognitive costs.

This entire, extremely complex section is the technical support for the biological load.

Summary: Linguistic difficulty triggers the release of stress/arousal neurotransmitters (norepinephrine) to help the brain cope, proving Mises's concept of "uneasiness."

Summary: This quote links "linguistic load" specifically to grammatical mismatches, providing evidence that non-standard grammar creates a burden for the reader.

Summary: This establishes the scientific definition of "transaction cost" in your paper: cognitive effort is physically measurable through pupil dilation.

Summary: This offers the solution: "segmentation." This proves that breaking information into standard, predictable chunks (Standardization) is the mechanism for preserving reader motivation.

Summary: Provides the hard statistical evidence that high transaction costs (cognitive load) destroy the specific type of motivation (extrinsic) required for professional and vocational success.

Summary: Defines "extraneous load" as a result of "unclear presentation," supporting the argument that non-standard writing (code-meshing) unnecessarily taxes the reader. This also aligns with findings from Liu.

Summary: Connects high cognitive load directly to "stress and frustration," validating the Misesian concept that "uneasiness" causes the consumer to exit the market (in this case, stop reading).

Summary: This offers empirical proof that structure (segmentation) directly correlates with "learning efficiency" and "profound understanding," serving as the scientific backing for the "Professional Imperative" of standardization.

Summary: This explains how structure helps: it allows readers to identify "natural boundaries." This validates the use of standard grammar conventions as necessary markers that help the brain group and process ideas, especially for those still learning the English language.

Summary: Provides the consequence of poor structure: "cognitive overload." This supports the argument that unstructured or non-standard writing risks overloading the reader, preventing them from understanding the core message.

Indirectly, this refutes the idea that "code-meshing" is necessary for more accurate communication.

Summary: Defines segmentation as a tool for managing cognitive processing, supporting the claim that structure is necessary for the reader (consumer) to handle the flow of information.

This helps to simplify the approach for those learning a new language, and reduces cognitive load.

This sentence serves as the thesis statement for the commentary, which defines the cause of the Karaja language's preservation. This is interesting because the reason for the preservation is not politics or economics, but to a "strong inner spiritual motivation" and deep cultural beliefs (the Aruana rites).

I can use this in an angle for synthesis by comparing the Karaja people's spiritual fight for language vitality with the personal and political fights for linguistic freedom detailed by other writers like Lyiscott and Baldwin.

80% of the study participants represent 12% of the world population, and do not represent the world population. That is pretty compelling evidence for bias in global research. This also validates the course's critique of monolingual ideology. Authors are trying to provide an alternative viewpoint that can counter the systemic prejudices discussed by Baldwin and Alvarez et al, who argue that standards are historically and ideologically constructed.

Framing: Use this quote (citing Henrich et al., the study mentioned by Maia and Gomes) to establish the Authority and Relevance of the source, demonstrating that the foundation of the standardization debate often rests on research that ignores the vast majority of human linguistic reality. Introduce the Karajá article as the necessary data point that corrects this bias by focusing on a non-WEIRD society.

Fascinating. This is a pretty important finding.

Framing: This is the most crucial direct evidence for my former thesis that code-meshing can lead to confusion. Introduce this finding using a strong signal phrase (e.g., "Maia and Gomes explicitly reported that..." or "Experimental research showed that...") to give the data weight. Immediately follow the quote by connecting it to my central claim: This finding suggests that demanding code-meshing in academic writing, as advocated by Young, risks fragmentation and slows comprehension for non-native English speakers (NNES).

Framing: Use this quote to emphasize the high Authority of the source (A in the CRAAP test). Frame it as a necessary support for the pragmatic stance. The four decades of research confirm that the study is based on sound methodology, meaning its structural findings on bilingual processing are reliable evidence that must govern the rhetorical choice between linguistic expression and structural necessity for efficient scholarly review.

Denouement/Resolution

Falling Action

Climax, reflections, falling action

points to a climax

overcoming obstacles

obstacle

obstacle

intro - the Exposition

interesting connection to the text

the emotional truth is the core of your messaging. Readers want to understand your experience, and most people view these as emotional journeys, many of which they can draw from their own personal emotional experiences that may be similar.

the voice, tone and content of your work should be authentically yours and also at the same time reflect the same internal voice inside the mind of your target audience, down to what they tell themselves about your topic, and maybe even touching on the same exact thoughts and phrases that keep them up late at night

tone is usually reflected in the language that you choose to send your message with. In this case, you should be able to deliver your message using the words of the audience that you are targeting.

the best content for your audience is data and research that is uniquely relevant to your target audience. the longer you research your audience, the clearer your messaging becomes.

this is a great model for approaching new environments -- it allows for one to focus explicitly on identifying the discourse community and integrating ourselves into that discourse community to make the transition more smooth.

This would especially be most valuable for those that are neurodivergent, especially Autism and ADHD, etc.

This is good advice -- not just for individual courses that we take from different Discourse Communities, but as we define our majors and focuses, it is important to join the discourse so that we can stay abreast of the latest information, discussions and practical solutions within it.

We can ask for help if needed to understand and execute academic writing, if needed.