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Record W4406545435 · doi:10.1177/0143831x241311406

Worker participation under digitalisation: Structure, power and varieties of union influence in two manufacturing sectors

2025· article· en· W4406545435 on OpenAlexafffundabout
Mathieu Dupuis, Alexis Massicotte

Bibliographic record

VenueEconomic and Industrial Democracy · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Economy and Work Transformation
Canadian institutionsUniversité Laval
FundersSocial Sciences and Humanities Research Council of CanadaFonds de Recherche du Québec-Société et Culture
KeywordsBusinessPower (physics)Industrial organizationManufacturing sectorLabour economicsEconomics

Abstract

fetched live from OpenAlex

Technological changes associated with the rise of digitalisation are sometimes envisioned as disrupting forces for the role of workers on the shopfloor. Whether these changes will lead to enhanced or reduced participation of labour and unions has been the subject of conjectures put forward by different schools of thought. The article proposes a framework mobilising various forms of participation to understand patterns of involvement under technological changes. An exploration of four cases in the aluminium and rubber manufacturing sectors in Québec (Canada) reveals a high variation of participation schemes, ranging from collective and formal to mixed (collective and formal, direct and informal), and direct and formal. The authors argue that patterns of participation at work should be understood as the outcomes of the material conditions (technology, markets, economic) in which workplaces are embedded in conjunction with the ability of workers and unions to mobilise power resources to influence changes at work.

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)0.006
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score0.171

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0030.007
Scholarly communication0.0040.001
Open science0.0010.004
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.018
GPT teacher head0.280
Teacher spread0.261 · 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

Citations5
Published2025
Admission routes3
Has abstractyes

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