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Record W3158486189

Executive-Employee Pay Gap and Academic Directors – A Chinese Study

2019· article· en· W3158486189 on OpenAlexaff
Fan Hong, Xiaofei Song, Lei Zhou

Bibliographic record

VenueSSRN Electronic Journal · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsSaint Mary's University
Fundersnot available
KeywordsExecutive compensationIncentiveCompensation (psychology)BusinessAccountingCompensation of employeesPoliticsPower (physics)Empirical researchPublic relationsCorporate governancePolitical sciencePsychologyEconomicsFinanceLaw
DOInot available

Abstract

fetched live from OpenAlex

This study investigates the impact of professor-directors on the executive-employee pay gap in public Chinese firms. University professors are generally believed to have higher standards of ethical and social responsibility by the public. Consistent with this view, we find there is a negative relationship between the executive-employee pay gap and the presence of professor-directors on board. This result is mainly driven by administrative professor directors. The strong political connections of the administrative professor-directors give them both the additional incentive and power to advocate for an executive-employee pay gap reduction. Empirical evidence seems to suggest that administrative professor-directors are successful in promoting employee pay while curbing executive compensation hikes, resulting a smaller executive-employee pay gap. On the other hand, the presence of non-administrative professor directors is associated with both higher executive compensation and higher employee pay, resulting in little change in the pay gap.

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)0.001
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.013
GPT teacher head0.244
Teacher spread0.231 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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