Genealogy of Refusal
Bibliographic record
Abstract
Why can’t librarians “Just Say No”? To answer this question, we look at workplace refusal through the fine arts, literature, and popular culture to construct a genealogy of workplace refusal. In it, we also begin to trace a lineage of crisis narrative critique alongside the library profession’s inheritance of vocational awe. We explore the librarian’s role and voice through the lens of both popular culture and academic publications. In our companion multimedia, hypertextual Scalar project also titled A Genealogy of Refusal: Walking Away from Crisis and Scarcity Narratives, we contextualize strategies of refusal in libraries through critical response to and annotations of film clips and illustrations. We examine gender differences in portrayals of workplace refusal. We laugh when in Parks and Recreation a stereotypical librarian ignores a stripper but warns noisy patrons: “Shh—This is a library!” We are horrified when aspiring librarians in Morgenstern’s Starless Sea, hands tied behind their backs, have their tongues torn from their mouths. Elinguation as a job prerequisite? No, thanks. The implications of saying “No” are many. We explicate ways librarians are made vulnerable by crisis narratives and constructed scarcity. We advocate for asset framing and developing fluencies in hearing and saying “No.” Looking forward, how long will it take librarians to reclaim “Yes” in a way that works for us?
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.018 | 0.041 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".