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Record W2154812988 · doi:10.5539/ibr.v7n3p72

Financial Distress and Corporate Governance: The Impact of Board Configuration

2014· article· en· W2154812988 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueInternational Business Research · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Distress and Bankruptcy Prediction
Canadian institutionsnot available
Fundersnot available
KeywordsBankruptcyCorporate governanceShareholderFinancial distressBusinessSample (material)AccountingLogitFinanceCorporate lawLogistic regressionEconomicsFinancial system

Abstract

fetched live from OpenAlex

The number of filings for bankruptcy procedures has exploded since 2007 and governance has been pointed outas one of the causes. We took a dataset of 312 US firms and asked the following research question: does theboard of directors configuration have an impact on financial distress? We used a matched-pair sample of USquoted firms with half of the sample filing for Chapter 7 (liquidation) or 11 (reorganization) of the United StatesBankruptcy Code and conducted logit regression analysis. We found that some board size was significantlydifferent for firms that opted for legal protection from those that did not. This study uses corporate governanceperspective to analyse the configuration of the board and its impact on a the decision of a company to resort to abankruptcy protection law. By demonstrating that corporate governance matters in terms of financial distress,this study offers guidance to shareholders and financial institutions.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.667
Threshold uncertainty score0.785

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.041
GPT teacher head0.311
Teacher spread0.270 · 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