Crisis Book Browsing: Restructuring the Retail Shelf Life of Books
Bibliographic record
Abstract
Crisis Book Browsing: Restructuring the Retail Shelf Life of BooksWhat happens when the traditional book shelving practices of physical bookstores are forced online? Within physical bookstores, the bookshelf operates as an organisational structure that rationalises the act of bookselling and helps consumers browse effectively (Rak 2012, Miller 2006). In born-digital book retail, algorithms often drive this process, functioning as organisational tools guided in part by a consumer’s browsing history (Murray 2018). COVID-19 has forced independent bookstores to rapidly rethink the effectiveness of how they organise and display books for consumers. Consumers who choose not to engage with born-digital book retailers, but for whom the physical bookstore’s bookshelf has become an object that can no longer be browsed in-person, need new solutions. Independent bookstores have been challenged to offer new affordances—including images and representations of shelves, e-commerce structures and algorithms—to help consumers stay connected with their browsing experiences. This paper examines three forms of browsable shelf experiences that have been developed in a time of crisis: bookstores virtually reimagining shelf experiences via Instagram, bookstores enhancing the accessibility and browsing experience of their websites, and bookstores posting unchanged images of their bookshelves to social media and asking customers to browse the books in those images. Using digital ethnography to examine these sites of crisis book browsing, and textual analysis of “COVID update” announcements, newly advertised job descriptions, and social media captions, this paper argues that COVID-19 has forced the restructure of shelf experiences in bookstores and that this will have long-term effects on book browsing into the future. References:Miller, Laura J. 2006. Reluctant Capitalists: Bookselling and the Culture of Consumption. University of Chicago Press.Murray, Simone. 2018. The Digital Literary Sphere: Reading, Writing, and Selling Books in the Internet Era. Johns Hopkins University Press.Rak, Julie. 2012. “Genre in the Marketplace: The Scene of Bookselling in Canada” in Ed. Anouk Lang. 2012. From Codex to Hypertext: Reading at the Turn of the Twenty-First Century. University of Massachusetts Press.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".