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Record W2171698265 · doi:10.1162/003465300558911

Competition within a Cartel: League Conduct and Team Conduct in the Market for Baseball Player Services

2000· article· en· W2171698265 on OpenAlexaff
D. G. Ferguson, Jason Jones, Kenneth G. Stewart

Bibliographic record

VenueThe Review of Economics and Statistics · 2000
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicSports Analytics and Performance
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsLeagueCartelCompetition (biology)Sample (material)EconomicsBusinessMicroeconomicsMarketingCollusion

Abstract

fetched live from OpenAlex

A model of major league baseball is developed which distinguishes between league behavior and individual team behavior. The league is viewed as setting rules that restrict the team's willingness to pay and/or impose costs on the transfer of players between teams. Given these rules, teams then compete for player services. The model is estimated and tested. The evidence suggests that the restrictive effect of league rules on player salaries declined between 1986-1988 and 1989-1991, consistent with anecdotal evidence. Within the rules established by the league, however, teams appear to behave as competitive price-takers through the entire sample period.

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)0.004
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 score0.065
Threshold uncertainty score0.130

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0070.002
Open science0.0020.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0150.002

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 head0.046
GPT teacher head0.259
Teacher spread0.213 · 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

Citations19
Published2000
Admission routes1
Has abstractyes

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