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Record W2397946227 · doi:10.1177/0160449x16647531

Assessing the Potential Impact of Labor Law Reforms on University Faculty

2016· article· en· W2397946227 on OpenAlexaffabout
Jonah Butovsky, Larry Savage, Michelle Webber

Bibliographic record

VenueLabor Studies Journal · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsBrock University
Fundersnot available
KeywordsLegislationOpposition (politics)LegislaturePoliticsPolitical scienceLawWork (physics)Collective bargainingLabour lawPublic administrationSociologyEngineering

Abstract

fetched live from OpenAlex

This article presents the findings of a survey of unionized professors and professional librarians at a public university in Southern Ontario to examine their views on the prospect and desirability of “right-to-work” legislation and “paycheck protection” laws. The purpose of the study is twofold: first, to assess the level of opposition to such legislative initiatives among unionized faculty, and, second, to determine the extent to which the passage of such laws would undermine the dues base of the faculty union. Based on the findings of a mixed methods survey, we found that a strong majority of the university professors and professional librarians surveyed were opposed to “right-to-work” and “paycheck protection” laws and that their passage would not deter them from paying dues or authorizing expenditures for political action.

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 imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.052
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.130
Threshold uncertainty score0.259

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.052
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0080.007
Scholarly communication0.0060.002
Open science0.0010.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.045
GPT teacher head0.393
Teacher spread0.348 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2016
Admission routes2
Has abstractyes

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