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Record W2810364375 · doi:10.1111/ijau.12125

Factors associated with internal audit's involvement in environmental and social assurance and consulting

2018· article· en· W2810364375 on OpenAlexaff
Dominic Canestrari-Soh, Nonna Martinov‐Bennie

Bibliographic record

VenueInternational Journal of Auditing · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsInstitute on Governance
FundersAccounting and Finance Association of Australia and New ZealandMacquarie University
KeywordsInternal auditBusinessAccountingAuditSustainabilityControl environmentInformation technology auditJoint auditContext (archaeology)Sustainability reportingCorporate governanceChief audit executiveAudit planPublic relationsEnvironmental resource managementCorporate social responsibilityPolitical scienceFinanceGeography

Abstract

fetched live from OpenAlex

Despite evidence of internal audit's expanding role in sustainability matters, there is limited understanding of factors associated with the extent of internal audit's involvement in these areas. This study examines the impact of governance factors, internal audit function characteristics, and organizational sustainability practices on the extent of internal audit's involvement in environmental and social assurance and consulting. The results suggest that management support and external reporting of sustainability information are key factors associated with internal audit's involvement in environmental and social assurance and consulting activities. The results also indicate that the extent of internal audit's involvement in assurance and consulting are not necessarily driven by a homogeneous set of factors. Future research could take a more nuanced approach to investigating different aspects of internal audit's roles both within the sustainability context and more broadly.

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)0.014
metaresearch head score (Gemma)0.065
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 score0.014
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.065
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.002
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.031
GPT teacher head0.257
Teacher spread0.226 · 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

Citations28
Published2018
Admission routes1
Has abstractyes

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