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Record W2893403795 · doi:10.1177/0143831x18793025

Managing workers’ capital? Limits and contradictions of labour investment funds

2018· article· en· W2893403795 on OpenAlexafffundabout
Ian Thomas MacDonald, Mathieu Dupuis

Bibliographic record

VenueEconomic and Industrial Democracy · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsUniversité TÉLUQUniversité du Québec à MontréalUniversité de Montréal
FundersUniversité de Montréal
KeywordsSolidarityInvestment (military)EconomicsCapital (architecture)Labour economicsFinanceCorporate governanceUmbrella fundBusinessMarket economyOpen-ended investment companyReturn on investmentLawPolitical scienceProduction (economics)Politics

Abstract

fetched live from OpenAlex

Labour-controlled investment is often touted as an alternative, pro-worker form of finance. Since 1983, the province of Quebec in Canada has experimented with workers’ participation in the form of workers’ funds controlled by the two major trade union federations. Drawing on research from secondary sources, archival material and semi-structured interviews, this article offers a comprehensive portrait of one of Quebec main workers’ funds, the FTQ Solidarity Fund. To date very little has been said about the impact of workers’ funds on firm governance, employment quality and labour relations. The article argues that any attempt to use investment to shape firm behaviour in the interests of workers and local unions is a limited and contradictory project. The argument is sustained through a discussion of the historical formation and institutional practices of the Solidarity Fund. The presence of large union-controlled investment funds offers local firms an alternative source of capital investment, protecting small and medium-sized firms from more aggressive financial actors. However, the main results show persistent tensions and contradictions between the Fund’s social goals and financial logics, as well as unintended effects on workplace union practices and power.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.828
Threshold uncertainty score0.780

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.038
GPT teacher head0.273
Teacher spread0.235 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations3
Published2018
Admission routes3
Has abstractyes

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