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Record W1579202314 · doi:10.7202/015797ar

From Person-Days Lost to Labour Militancy

2007· article· en· W1579202314 on OpenAlexaffvenueabout
Linda Briskin

Bibliographic record

VenueRelations industrielles · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsYork University
Fundersnot available
KeywordsPerspective (graphical)PoliticsGovernment (linguistics)Work (physics)Presentation (obstetrics)Point (geometry)Data collectionFocus (optics)Political scienceSociologyPolitical economySocial scienceLawEngineeringComputer science

Abstract

fetched live from OpenAlex

Using the micro-data from Human Resources and Social Development Canada (HRSDC) on the 23,944 stoppages in Canada between 1960 and 2004, this article introduces a labour militancy perspective on work stoppages, that is, from the point of view of workers. It explores patterns of militancy with a focus on strike duration, strike size and strikes for first contracts, and supports re-interpretations which help make visible the significance of such stoppages for workers, unions and communities. A labour militancy frame presents an alternative to the employer perspective on time lost, the government concern to measure the economic impact of stoppages, and the scholarly emphasis on strike determinants. As part of re-examining the HRSDC work stoppage data from a labour militancy perspective, the paper considers the source of these data. It juxtaposes the statistical data with interviews with the provincial correspondents who collect the information for HRSDC. Examining the data in this light underscores the political nature of data collection (what is seen to be germane and not), data presentation (what is made visible and what is not), and data sources (whose voices are heard).

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.804
Threshold uncertainty score0.998

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.031
GPT teacher head0.294
Teacher spread0.263 · 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

Citations9
Published2007
Admission routes3
Has abstractyes

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