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Record W2045283524 · doi:10.1177/2329496514558626

Employment Discrimination Lawsuits and Corporate Stock Prices

2015· article· en· W2045283524 on OpenAlex
C. Elizabeth Hirsh, Youngjoo Cha

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

VenueSocial Currents · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsLawsuitPlaintiffHuman settlementStock (firearms)CommissionBusinessStock priceLitigation risk analysisLawEconomicsFinanceAccountingPolitical science

Abstract

fetched live from OpenAlex

In this study, we examine the financial impact of employment discrimination lawsuit verdicts and settlements on publicly traded firms subject to lawsuits between 1997 and 2008. Using data on 174 sex and race discrimination lawsuits involving 107 publicly traded companies, we assess the effect of lawsuit verdicts and settlements on changes in defendants’ daily stock returns. Findings indicate that verdicts and settlements have an immediate negative impact on defendants’ stock prices. In addition, the negative effect is more pronounced among cases that involve monetary payouts, cases in which the U.S. Equal Employment Opportunity Commission is a plaintiff and cases that involve sex as opposed to race or national origin discrimination. These results demonstrate the extent to which legal rulings introduce a market penalty for employers and have implications for the study of law, organizations, and market responses to discriminatory behavior.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.086
Threshold uncertainty score0.430

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.118
GPT teacher head0.285
Teacher spread0.166 · 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