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Record W7133585134

Creative Tactics and Union Politics. Learning from the York University Strike

2010· article· W7133585134 on OpenAlexaboutno aff
Roberta Buiani

Bibliographic record

VenueODU Digital Commons (Old Dominion University) · 2010
Typearticle
Language
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsnot available
Fundersnot available
KeywordsBureaucracySalaryCertificationAdministration (probate law)RhetoricEmployabilityHigher educationIndustrial relations
DOInot available

Abstract

fetched live from OpenAlex

[First paragraph] The third-largest university in Canada, York University owes its fame to a long history of labor disputes. Historically, the neoliberal rhetoric and goals of York’s intransigent administration have always clashed with a radical body of graduate students and faculty, who not only deemed it politically and ethically necessary to challenge the employer and reclaim their rights, but also considered it a question of survival. Certified as a union since the mid-Seventies, York’s contract faculty and teaching assistants (then joined in 2001 by graduate assistants) [1] have been periodically engaged against the institution’s trend towards the progressive déclassement [2] of their qualifications and professional expertise, and the casualization of academic jobs (Vercellone 2009, p. 123). A great deal of tenacity and a fast increasing union membership helped them endure often lengthy and sometimes disastrous strikes. Despite mixed results, their union, known today as CUPE Local 3903, neither succumbed to fatigue and loss—using losses and victories equally to its own advantage to improve its members’ salary and benefit package—nor did it morph into a stiff bureaucratic machine and managing its internal conflicts openly and as creatively as possible—refusing to comply to an organizational structure forever frozen into a set of default rules.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.921
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0040.002
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0000.001
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 head0.016
GPT teacher head0.216
Teacher spread0.200 · 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 designTheoretical or conceptual
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
Published2010
Admission routes1
Has abstractyes

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