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Record W2964155879 · doi:10.54648/bula2019020

Corporate Governance and Climate Change: Smoothing Temporal Dissonance to a Phased Approach

2019· article· en· W2964155879 on OpenAlexaff
Stelios Andreadakis, Lisa Benjamin

Bibliographic record

VenueBusiness Law Review · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsDalhousie University
Fundersnot available
KeywordsCorporate governanceCognitive dissonanceClimate changeContext (archaeology)BusinessClimate change mitigationEnergy transitionEconomicsEnvironmental resource managementFinanceGeographyPsychology

Abstract

fetched live from OpenAlex

SUMMARY Projections for climate change extend decades into the future, and usually to the end of this century due to the long-lived nature of greenhouse gases (GHGs). Predominant normative frameworks for corporate governance are primarily short-term in nature, creating a temporal dissonance within the context of corporate governance and climate change. Adding to this complexity, the energy transition itself has temporal paradoxes and implications for the global economy – the transition away from fossil fuels cannot be too sudden and sharp, but an urgent yet stable, phased transition is required. Statutory interventions in the UK have imposed on directors the requirement to consider the long-term profitability of companies. New initiatives, such as the task force on climate related disclosures (TCFD), the Enterprise Principles, and the Oxford-Martin Principles, also advocate for directors to consider the risks from climate change, including emissions scenarios which take into account short-, medium- and long-term scenarios. It is by using a phased approach to climate risk that a smoothing of this temporal dissonance between corporate governance and climate change can be initiated by businesses. While many of these new governance initiatives do not yet provide the requisite level of specificity to demonstrate how a phased approach could be adopted by particular companies, the TCFD guidance does provide some tools which would allow companies to adopt a phased approach, however the types and levels of detail of these tools should be increased for a variety of types of industry.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.797
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.062
GPT teacher head0.279
Teacher spread0.217 · 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 designNot applicable
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
Published2019
Admission routes1
Has abstractyes

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