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Record W3016019618 · doi:10.20900/jsr20200019

Business Sustainability and Insolvency Proceedings—The EU Perspective

2020· article· en· W3016019618 on OpenAlexaboutno aff
Tuula Linna

Bibliographic record

VenueJournal of Sustainability Research · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Insolvency and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsInsolvencySustainabilityProfitability indexSustainability organizationsBusinessCreditorRestructuringBankruptcyAccountingFinanceEconomicsDebt

Abstract

fetched live from OpenAlex

Business sustainability refers to economic sustainability performance that promotes profitability and to non-financial sustainability that may or may not create profitability. This paper concentrates on the relation between business sustainability and insolvency proceedings by asking what role business sustainability plays when choosing between restructuring (rescue) and liquidation proceedings. This choice is based on two tests: a viability test and a best interest of creditors test, the latter meaning that no dissenting creditor should be worse off in restructuring than in liquidation proceedings. In addition, the paper asks what role business sustainability plays when making the choice between restructuring and liquidation and what the consequences of this choice are for business sustainability elements. In addition, the paper asks who the stakeholders for business sustainability are in insolvency situations. The finding of the study is that creditor interest should be better balanced with non-financial sustainability, but with the requirement that creditors know the risks beforehand and are able to protect their interests, for example, through securities. Regarding environmental hazards, the paper suggests a “super responsibility” of bankruptcy estates to handle environmental problems, according to the precedent of the Canadian Supreme Court.

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)0.005
metaresearch head score (Gemma)0.022
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.242
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0000.001
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 head0.058
GPT teacher head0.326
Teacher spread0.267 · 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.

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

Citations10
Published2020
Admission routes1
Has abstractyes

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