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Record W2274122977

Solidarity Forever? Trends in Canadian Union Density

2004· article· en· W2274122977 on OpenAlexvenueaboutno aff
Andrew Jackson

Bibliographic record

VenueStudies in Political Economy · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsnot available
Fundersnot available
KeywordsSolidarityUnion densityDemocracyPoliticsPolitical sciencePolitical economyEconomic historySociologyPublic administrationCollective bargainingLawEconomics
DOInot available

Abstract

fetched live from OpenAlex

Unions are a key force for a more equal and democratic society. Clearly, union influence within and outside of the workplace is partly a function of union density, or the proportion of workers who belong to unions. In Canada, union density today is much higher than in the United States, and has held up better than in many other advanced industrial countries. Nonetheless, density has slipped to just one in three workers and fewer than one in five private sector workers. In October 2003, the Canadian Labour Congress held an important mid-term conference on union organizing, bringing together researchers, union leaders, and activists to debate trends and strategies. The paper by CLC senior economist Andrew Jackson summarizes background research for the conference, and flags some of the main issues which emerged in discussion. It is followed by summaries of contributions to the conference made by Pradeep Kumar, Gregor Murray, and Charlotte Yates, and reflections on the conference by Ontario Federation of Labour Research Director Chris Schenk. Studies in Political Economy 124

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)0.003
metaresearch head score (Gemma)0.013
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 score0.138
Threshold uncertainty score1.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0060.017
Science and technology studies0.0110.003
Scholarly communication0.0050.002
Open science0.0030.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0110.001

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 head0.043
GPT teacher head0.361
Teacher spread0.319 · 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

Citations1
Published2004
Admission routes2
Has abstractyes

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