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Record W7096838915

PRELIMINARY AND INCOMPLETE

2005· article· en· W7096838915 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicLabor market dynamics and wage inequality
Canadian institutionsnot available
Fundersnot available
KeywordsLabor relationsShareholderVariance (accounting)WageIndustrial relationsQuality (philosophy)Complete information
DOInot available

Abstract

fetched live from OpenAlex

The extent to which firm ownership is concentrated or dispersed varies considerably from country to country. Explanations based on differences in the protection of minor-ity shareholders leave a significant part of the variance unexplained. This paper offers a novel explanation for the variation in ownership concentration across countries based on differences in the quality of labor relations. We show empirically that countries in which labor relations are hostile tend to have more concentrated ownership. These results con-tinue to hold when we instrument labor relations using religion, which has been argued is a key factor in the history of European labor organizations. We find similar results using historical data for Canada, documenting a surprisingly strong correlation between strike activity and ownership concentration over the past 50 years. Theoretically, we show that these findings are consistent with a simple model of repeated wage bargaining under asymmetric information in which labor relations and ownership concentration are jointly determined.

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)0.008
metaresearch head score (Gemma)0.056
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.582
Threshold uncertainty score0.831

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.056
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.010
Science and technology studies0.0040.002
Scholarly communication0.0070.007
Open science0.0030.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.4180.171

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 head0.025
GPT teacher head0.218
Teacher spread0.193 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2005
Admission routes1
Has abstractyes

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