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Record W4411890989 · doi:10.1002/iir.70006

Power, choice, exposure and fragility: Reframing fairness in equity for the corporate and insolvency sphere

2025· article· en· W4411890989 on OpenAlexvenueno aff
Jennifer Gant

Bibliographic record

VenueInternational Insolvency Review · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Law and Human Rights
Canadian institutionsnot available
Fundersnot available
KeywordsInsolvencyCreditorDebtorShareholder primacyLaw and economicsRedressCognitive reframingEconomicsEquity (law)BusinessDebtLawCorporate lawFinancePolitical scienceCorporate governance

Abstract

fetched live from OpenAlex

Abstract Where is the place of humanity in current corporate and insolvency frameworks and their theoretical underpinning? How can it be assured that the institutions that have been invented through human ingenuity and brilliance serves the collective human experience fully and equitably? Insolvency law has long been theoretically conceptualised on the basis of legal and contractual entitlements with the core purpose of maximising returns to the creditors owed by the debtor during its financial distress. With the onset and steady growth of the rescue culture with its acknowledgment of the broader impacts of insolvency on society and the economy, the simple creditor wealth maximisation approach does not provide equitable solutions for all of the stakeholder associated with the debtor company. In particular, involuntary, non‐adjusting, and undiversified creditors, such as employees, tort creditors, and the environment, are often with little recourse and few opportunities for participation at the negotiation table. These stakeholders suffer from greater exposure and fragility in their corporate relationships with little power or choice on that relationship and its impact on them. If theories are devised with humans in mind, it may be possible to redress the inequity in fairness by changing the way we view financial distress and the broader purpose for resolving it. This article seeks to reframe the way fairness is assessed and applied to achieve equitable solutions in the corporate and insolvency sphere by examining it through a feminist jurisprudential lens, with a particular focus on Martha Fineman's Vulnerability Theory.

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 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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.678
Threshold uncertainty score0.543

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.047
GPT teacher head0.312
Teacher spread0.265 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations1
Published2025
Admission routes1
Has abstractyes

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