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Record W3125789094

Environmental Performance, Environmental Risk and Risk Management

2013· article· en· W3125789094 on OpenAlexaffabout
Michael Dobler, Kaouthar Lajili, Daniel Zéghal

Bibliographic record

VenueSSRN Electronic Journal · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEnvironmental Sustainability in Business
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsRisk managementBusinessEnvironmental management systemSample (material)Risk assessmentEnterprise risk managementEnvironmental risk assessmentFinancial risk managementFactor analysis of information riskIT risk managementActuarial scienceEnvironmental resource managementRisk analysis (engineering)EconomicsRisk management information systemsEngineeringFinanceManagement
DOInot available

Abstract

fetched live from OpenAlex

Purpose This paper aims to investigate the attributes and information content of risk reporting in two different institutional and regulatory, namely, Canadian and German, settings during the period surrounding the financial crisis of 2008. Design/methodology/approach For a matched sample of manufacturing firms in the period 2006–2010, this study conducts a detailed content analysis of annual reports to assess and compare the volume and patterns of risk disclosures. Panel regressions are used to explore how risk disclosures related to corporate risk proxies and performance indicators. Findings Over the sample period, Canadian and German firms increase the volume but largely maintain the patterns of risk disclosures. Risk disclosures relate to corporate risk proxies but are not incrementally informative to assess firm performance. Originality/value The paper contributes to research on risk reporting by providing detailed cross-country evidence for a period particularly shaped by significant risk. The findings have implications for the regulation and usefulness of risk reporting.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.124
Threshold uncertainty score0.247

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.002
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.002
GPT teacher head0.154
Teacher spread0.152 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations5
Published2013
Admission routes2
Has abstractyes

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