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Record W4313396111 · doi:10.1111/1911-3838.12330

Rethinking Accounting, Accountability, and Accounting Regulation: Concerns about the Proposed Canadian Sustainability Standards Board*

2022· article· en· W4313396111 on OpenAlexaffvenueabout
D. James Cooper, Jeff Everett, Darlene Himick, Daniela Senkl

Bibliographic record

VenueAccounting Perspectives · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsUniversity of OttawaYork UniversityUniversity of GuelphUniversity of Alberta
Fundersnot available
KeywordsAccountingSustainability reportingSustainabilityAccountabilityEnforcementAccounting standardNeglectPolitical scienceFinancial accountingProcess (computing)BusinessPublic relationsEconomicsAccounting information systemCorporate social responsibilityLawPsychologyComputer science

Abstract

fetched live from OpenAlex

Abstract This paper expands on a letter recently submitted by a group of Canadian business academics to the Independent Review Committee on Standard Setting in Canada (IRCSSC) in response to the committee's proposed Canadian Sustainability Standards Board. We highlight sections of the IRCSSC's Consultation Paper that we find problematic and draw on accounting and other research to explain why it fails to live up to its potential. Chief among the problems we identify is that the IRCSSC appears to be wedded to the same narrow, investor‐based focus promoted by the International Sustainability Standard Board. We also draw attention to the rushed nature of the process, its exclusion of lay experts, the IRCSSC's ambiguous use of the term public interest , and its inattention to alternative understandings of value and the environment (including the people within it). Finally, we problematize the IRCSSC's sidestepping of the issues of power, culture, and conflict; its neglect of monitoring and enforcement; and its surprising disregard of the Global Reporting Initiative. Along with a number of suggestions for improving the process and its outcome, this paper also contributes to ongoing debates on standard setting and the question of whether accounting is currently equipped to provide the necessary tools for sustainability 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 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)0.013
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.408
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0080.001
Scholarly communication0.0030.004
Open science0.0010.002
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.288
Teacher spread0.266 · 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 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

Citations8
Published2022
Admission routes3
Has abstractyes

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