Bibliographic record
Abstract
How students get the materials they need as opportunities for higher education expand but funding shrinks.From the top down, Shadow Libraries explores the institutions that shape the provision of educational materials, from the formal sector of universities and publishers to the broadly informal ones organized by faculty, copy shops, student unions, and students themselves. It looks at the history of policy battles over access to education in the post–World War II era and at the narrower versions that have played out in relation to research and textbooks, from library policies to book subsidies to, more recently, the several “open” publication models that have emerged in the higher education sector.From the bottom up, Shadow Libraries explores how, simply, students get the materials they need. It maps the ubiquitous practice of photocopying and what are—in many cases—the more marginal ones of buying books, visiting libraries, and downloading from unauthorized sources. It looks at the informal networks that emerge in many contexts to share materials, from face-to-face student networks to Facebook groups, and at the processes that lead to the consolidation of some of those efforts into more organized archives that circulate offline and sometimes online— the shadow libraries of the title. If Alexandra Elbakyan's Sci-Hub is the largest of these efforts to date, the more characteristic part of her story is the prologue: the personal struggle to participate in global scientific and educational communities, and the recourse to a wide array of ad hoc strategies and networks when formal, authorized means are lacking. If Elbakyan's story has struck a chord, it is in part because it brings this contradiction in the academic project into sharp relief—universalist in principle and unequal in practice. Shadow Libraries is a study of that tension in the digital era.ContributorsBalázs Bodó, Laura Czerniewicz, Miroslaw Filiciak, Mariana Fossatti, Jorge Gemetto, Eve Gray, Evelin Heidel, Joe Karaganis, Lawrence Liang, Pedro Mizukami, Jhessica Reia, Alek Tarkowski
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.018 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.754 | 0.632 |
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".