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Record W3215478694 · doi:10.1177/09596801211056836

Rationalizing the irrational: Making sense of (in)consistency among union members and non-members

2021· article· en· W3215478694 on OpenAlexaff
Sinisa Hadziabdic, Lorenzo Frangi

Bibliographic record

VenueEuropean Journal of Industrial Relations · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsIdeologyWageConsistency (knowledge bases)Irrational numberPoliticsDemographic economicsSingle marketEconomicsPolitical sciencePositive economicsLabour economicsPolitical economyEuropean unionLawInternational trade

Abstract

fetched live from OpenAlex

Focusing on 13 OECD countries over 25 years, we examine the factors that explain why a sizable fraction of wage-earners exhibit an inconsistency between their union membership status and their confidence in unions by being either confident non-members or non-confident members. While structural factors associated with joining constraints generate inconsistency in specific labour market categories, wage-earners who have extreme ideological orientations and are highly interested in politics are much less likely to exhibit inconsistency across time and countries. For individuals who have intermediate ideological orientations and are not very interested in politics, differences in terms of non-member and member inconsistency between countries are explainable through contextual variables such as economic conditions, the level of employment protection, and historical legacies. Implications for union membership research and union strategies 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 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)0.014
metaresearch head score (Gemma)0.047
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.047
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.008
Scholarly communication0.0060.004
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.049
GPT teacher head0.292
Teacher spread0.243 · 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

Citations11
Published2021
Admission routes1
Has abstractyes

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