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Record W4200331597 · doi:10.33137/cjalrcbu.v7.36442

Genealogy of Refusal

2021· article· en· W4200331597 on OpenAlexafffundvenue
Natalie Meyers, Anna Michelle Martinez-Montavon, Mikala Narlock, Kim Stathers

Bibliographic record

VenueCanadian Journal of Academic Librarianship · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Relations and Crisis Communication
Canadian institutionsUniversity of Northern British Columbia
FundersUniversity of Northern British ColumbiaUniversity of Notre DameUniversity of British ColumbiaUniversity of Wisconsin-Milwaukee
KeywordsNarrativeMedia studiesFraming (construction)ScarcitySociologyHistoryLiteratureArt

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.883
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.064
GPT teacher head0.321
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2021
Admission routes3
Has abstractyes

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