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Record W7132909446

Students' perceptions of fairness following an academic strike

2012· article· en· W7132909446 on OpenAlexafffundabout
Lisa Fıksenbaum, Christine M. Wickens, Esther R. Greenglass, David L. Wiesenthal

Bibliographic record

VenueTSpace · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicAcademic Freedom and Politics
Canadian institutionsYork University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPerceptionHigher educationSurvey data collectionJob satisfactionMultilevel model
DOInot available

Abstract

fetched live from OpenAlex

Rising rates of unionization in university settings suggest that campus labour disputes are likely to become an increasingly relevant issue. The research question in the current analysis asked which factors contributed to students’ perception of fair treatment following a university labour disruption. A longitudinal survey of students’ experiences was conducted before, during, and following a 12-week strike by teaching assistants and contract faculty at a large Canadian university. Hierarchical regression analysis revealed that students’ pre-strike satisfaction with their academic program contributed to a perception of post-strike fairness. The more students’ plans had been affected by the strike, the greater the reduction in perceived fairness. Post-strike fairness increased significantly the more students were satisfied with course remediation and the more they felt they had a faculty member to turn to following the strike. Interestingly, neither students’ levels of financial concern, nor their attitudes toward the strike, predicted perceived fairness. Implications for addressing students’ concerns in the wake of an academic labour dispute are discussed.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.397
Threshold uncertainty score0.410

Codex and Gemma teacher scores by category

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

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 head0.075
GPT teacher head0.473
Teacher spread0.398 · 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 designQualitative
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
Published2012
Admission routes3
Has abstractyes

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