MétaCan
Menu
Back to cohort
Record W3172032687 · doi:10.1177/00197939211015191

Recentralizing Industrial Relations? Local Unions and the Politics of Insourcing in Three North American Automakers

2021· article· en· W3172032687 on OpenAlexaffabout
Mathieu Dupuis, Ian Greer

Bibliographic record

VenueIndustrial and Labor Relations Review · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsUniversité TÉLUQUniversité du Québec à Montréal
FundersAutomotive Research Center
KeywordsInsourcingCorporationOutsourcingPoliticsPurchasingWork (physics)ScholarshipAutomotive industryIndustrial relationsBusinessEconomyEconomicsMarketingMarket economyIndustrial organizationManagementPolitical scienceEngineeringEconomic growthFinanceLaw

Abstract

fetched live from OpenAlex

Since the auto industry’s 2008 crisis, the decades-long trend toward outsourcing by the Detroit Three automakers has stalled. During and after the crisis, original equipment manufacturers moved work inside their corporate boundaries, including the purchase of eight previously spun-off parts plants. Why has this happened? Drawing on 77 interviews in the United States and Canada and 27 insourcing cases, the authors explore how and why insourcing has taken place. Past literature has considered the costs and benefits of creating the vertically integrated corporation, the managerial politics behind vertical disintegration, and the labor–management relations that shape both. While much industrial relations scholarship points to decentralized plant-level partnerships as a union strategy to win investment, the authors find that local unionists are intervening in the politics of the corporation above the plant level to influence the purchasing, manufacturing, and engineering functions that determine the sourcing decision.

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)0.005
metaresearch head score (Gemma)0.003
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 score0.116
Threshold uncertainty score0.230

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0130.007
Scholarly communication0.0050.002
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.050
GPT teacher head0.303
Teacher spread0.253 · 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

Citations7
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueIndustrial and Labor Relations ReviewSame topicLabor Movements and UnionsFrench-language works237,207