Cut/Copy/Paste explores the relations between fragments, history, books, and media. It does so by scouting out fringe maker cultures of the seventeenth century, where archives were cut up, “hacked,” and reassembled into new media machines: the Concordance Room at Little Gidding in the 1630s and 1640s, where Mary Collett Ferrar and her family sliced apart printed Bibles and pasted the pieces back together into elaborate collages known as “Harmonies”; the domestic printing atelier of Edward Benlowes, a gentleman poet and Royalist who rode out the Civil Wars by assembling boutique books of poetry; and the nomadic collections of John Bagford, a shoemaker-turned-bookseller who foraged fragments of old manuscripts and title pages from used bookshops to assemble a material history of the book. Working across a century of upheaval, when England was reconsidering its religion and governance, each of these individuals saved the frail, fragile, frangible bits of the past and made from them new constellations of meaning. These fragmented assemblages resist familiar bibliographic and literary categories, slipping between the cracks of disciplines; later institutions like the British Library did not know how to collate or catalogue them, shuffling them between departments of print and manuscript. Yet, brought back together in this hybrid history, their scattered remains witness an emergent early modern poetics of care and curation, grounded in communities of practice. Stitching together new work in book history and media archaeology via digital methods and feminist historiography, Cut/Copy/Paste traces the lives and afterlives of these communities, from their origins in early modern print cultures to the circulation of their work as digital fragments today. In doing so, this project rediscovers the odd book histories of the seventeenth century as a media history with an ethics of material making—one that has much to teach us today.

The manifold hypothesis is that natural data forms lower-dimensional manifolds in its embedding space.

Projects like the Open Journal System, Manifold or Scalar are based on a distributed model that allow anyone to download and deploy the software (Maxwell et al., 2019), offering an alternative to the commercial entities that dominate the scholarly communication ecosystem.

ey use local computations to interpolate over task-rele-vant manifolds in a high-dimensional parameter space.

One popular theory among machine learning researchers is the manifold hypothesis: MNIST is a low dimensional manifold, sweeping and curving through its high-dimensional embedding space. Another hypothesis, more associated with topological data analysis, is that data like MNIST consists of blobs with tentacle-like protrusions sticking out into the surrounding space.

A Tutorial on Distance Metric Learning: Mathematical Foundations, Algorithms and Software

Grassmannian Learning: Embedding Geometry Awareness in Shallow and Deep Learning

Classification and Geometry of General Perceptual Manifolds

UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

We are asking authors to consider, from the very beginning of the research process, developing and sharing their project iteratively. Research materials, filmed images, field notes, ethnographic materials, sketches, maps, audio recordings, interviews, and other forms of research that are used to write the monograph will have a place on Manifold so that scholars can share their work as it is being researched and written.

Our goal is to create a platform that will rival the reading experience of a commercial site like Medium but that is free and open-source.