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Record W2796359822 · doi:10.1177/0160449x18763442

Community Unionism without the Community? Lessons from Labor-Community Coalitions in the Canadian Child Care Sector

2018· article· en· W2796359822 on OpenAlexaffabout
Simon Black

Bibliographic record

VenueLabor Studies Journal · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsBrock University
Fundersnot available
KeywordsAusterityContext (archaeology)Public sectorPolitical sciencePublic administrationCommunity organizationPraxisEconomic growthLabor relationsPublic spaceSociologyPublic relationsEconomicsLawPoliticsGeography

Abstract

fetched live from OpenAlex

The theory and practice of community unionism has been central to discussions of alt-labor, union renewal, and revitalization, particularly in relation to union praxis at the urban or local scale. This comparative case study explores two labor-community campaigns to defend public child care services in the context of neoliberal austerity in urban/suburban space. While labor-community coalitions are a necessary—if not sufficient—condition for success, in urban/suburban contexts in which community allies are weak and municipal administrations hostile, public-sector unions must continue to play a leading role in campaigns despite the risk of being cast as defenders of sectional interests rather than of the public good. In such contexts, union involvement in community organizing is a necessary precursor to successful labor-community campaigns.

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)0.006
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.092
Threshold uncertainty score0.669

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0450.033
Scholarly communication0.0120.006
Open science0.0030.009
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0090.000

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 head0.090
GPT teacher head0.385
Teacher spread0.295 · 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 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

Citations4
Published2018
Admission routes2
Has abstractyes

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