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Record W4384282546 · doi:10.2308/horizons-2020-207

Do Gender-Diverse Boards Enhance the Linguistic Features of Corporate Financial Reporting?

2023· article· en· W4384282546 on OpenAlex

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

VenueAccounting Horizons · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsHEC MontréalUniversity of Ottawa
FundersMinisterio de Ciencia e Innovación
KeywordsReadabilityAccountingTone (literature)Gender diversityCorporate governanceAuditBusinessTransparency (behavior)Representation (politics)Quality (philosophy)Diversity (politics)Sample (material)NarrativePolitical scienceFinanceLinguistics

Abstract

fetched live from OpenAlex

SYNOPSIS Textual features, such as readability and disclosure tone, of mandatory financial reports have significant economic consequences. Managers and directors’ demographic attributes can also lead to different reporting styles. This study examines how gender-diverse boards influence the readability and tone of corporate financial disclosures under the framework of upper echelons theory. Using a sample of 3,085 U.S. firm-year observations from 2007 to 2016, we find that gender diversity in the board and audit committee enhances the readability of narrative disclosures and is associated with a less optimistic, litigious, and ambiguous tone in annual reports. This study highlights the contribution of female directors to the quality and transparency of financial disclosures and supports recent regulatory initiatives aimed at enhancing female representation on corporate boards.

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)0.073
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.264
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.073
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.029
GPT teacher head0.260
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