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Record W4318573546 · doi:10.33137/cjal-rcbu.v8.38853

A Seat at the Table

2023· article· en· W4318573546 on OpenAlexaffvenueabout
Anthony Pash, Erin Patterson

Bibliographic record

VenueCanadian Journal of Academic Librarianship · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Administration
Canadian institutionsAcadia University
Fundersnot available
KeywordsNegotiationAssociation (psychology)Round tableCollective bargainingWorkloadPublic relationsTable (database)Political scienceUnit (ring theory)Professional associationFace (sociological concept)PsychologySociologyManagementBusinessLaw

Abstract

fetched live from OpenAlex

This paper reports on research into librarian participation on faculty association executive and collective bargaining teams at 46 Canadian universities at which librarians are in the same bargaining unit as professors. The goal of this study is to determine the extent of such participation on these key committees, whether such participation is mandated by governing documents or a matter of custom (or neither), and what barriers librarians face to such participation. The authors analyzed these associations’ constitutions and bylaws and then conducted interviews with faculty association leaders and librarian activists. Findings indicate that nearly half of the surveyed associations either have a mandated seat for librarians or make every effort to include librarians on their executive committees, and more than a third do the same for their collective bargaining teams. Many associations have had a librarian as faculty association president and a handful have had a librarian as chief negotiator. The most-cited barriers to taking on these leadership roles in the association are workload and the lack of or unsuitability of course release for librarians. The level of librarian participation in faculty associations across Canada is very encouraging, but many issues need to be addressed if librarians are to have a full seat at the table.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.314
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.003
Open science0.0010.000
Research integrity0.0000.000
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 head0.078
GPT teacher head0.303
Teacher spread0.224 · 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.

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

Citations0
Published2023
Admission routes3
Has abstractyes

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