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Record W2961523731 · doi:10.1111/bjir.12484

Partnering against Insecurity? A Comparison of Markets, Institutions and Worker Risk in Canadian and Swedish Retail

2019· article· en· W2961523731 on OpenAlexfundaboutno aff
Sean O’Brady

Bibliographic record

VenueBritish Journal of Industrial Relations · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsnot available
FundersFonds de Recherche du Québec-Société et CultureHandelsrådet
KeywordsLeverage (statistics)Competition (biology)BusinessLabour economicsTrade unionBargaining powerEuropean unionEconomicsMarket economyEconomic policy

Abstract

fetched live from OpenAlex

Abstract This article provides insights on how union power influences the outcomes of labour‐management partnerships, with a focus on insecurity. It examines matched pairs of food retailers in Canada and Sweden. Trends in wages, scheduling and union coverage from 1980 to 2016 are compared. Actors in both contexts adopted partnering strategies in response to intensified price competition. However, the Swedish partnerships produced stable work arrangements, while working conditions eroded considerably in Canada. Bargaining structures, union security and identity are examined to explain the variations. As market competition intensified, the Swedish union gained leverage by using partnerships to fight unfair competition and promote sectoral well‐being in the process. Meanwhile, the Canadian union lost leverage, instead using partnerships to align employment practices with those of low‐cost market entrants and enhancing store‐level performance at all costs. The argument is that markets can be a resource for unions, even in low‐skilled service sectors, but only under inclusive institutions.

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)0.002
metaresearch head score (Gemma)0.005
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 score0.977
Threshold uncertainty score0.164

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.006
Science and technology studies0.0060.003
Scholarly communication0.0060.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.045
GPT teacher head0.301
Teacher spread0.256 · 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

Citations7
Published2019
Admission routes2
Has abstractyes

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