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Record W1989607612 · doi:10.4284/0038-4038-2011.215

The CEO Arms Race

2012· article· en· W1989607612 on OpenAlex
Natalia Gritsko, Valentina Kozlova, William S. Neilson, Bruno Wichmann

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSouthern Economic Journal · 2012
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic theories and models
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsRace (biology)Chief executive officerArms raceOfficerBusinessProfit (economics)Outcome (game theory)Position (finance)MicroeconomicsLabour economicsEconomicsManagementFinancePolitical sciencePolitical economyLawSociology

Abstract

fetched live from OpenAlex

This article constructs a game‐theoretic model in which high chief executive officer (CEO) pay emerges as the outcome of an arms race, with each firm hiring a highly paid CEO to protect its competitive position against rivals who also hire highly paid CEOs. For an arms race to emerge, highly paid CEOs must generate idiosyncratic, privately known internal effects on profit, and CEO pay disparities must also generate asymmetric profit differences from external effects beyond the simple differences in pay. If the distribution of internal effects satisfies a key uniformity condition, an arms race emerges as the only equilibrium of the game.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.708
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.015

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 head0.022
GPT teacher head0.203
Teacher spread0.181 · 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