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Record W4214632120 · doi:10.5465/amle.2021.0182

Embedding a “Reflexive Mindset”: Lessons From Reconfiguring the Internal Auditing Practice

2022· article· en· W4214632120 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAcademy of Management Learning and Education · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsWestern University
Fundersnot available
KeywordsInternal auditReflexivityMindsetAuditInternal controlCorporate governanceBusinessProcess managementPublic relationsKnowledge managementAccountingSociologyComputer sciencePolitical science

Abstract

fetched live from OpenAlex

The education and training of internal auditors is an example of management learning which has received limited attention in management education journals. This paper presents the lessons from an action research inquiry designed to reconfigure the Internal Auditing function to address the problem of a conformance mindset and compliance-based approach. We show how cultivating a ‘reflexive mindset’ becomes a critical catalyst in developing an Internal Auditing approach that leads to the identification of misconduct, conduct risk and deficiencies in the organization’s conduct risk management and governance frameworks. We contribute to advance reflexivity as a practice that can support the reconfiguration of management functions like Internal Auditing, not only by readjusting operating routines but also by encouraging internal auditors to critically (re)consider how their activities may contribute to the common good of the organization’s members and customers.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.800
Threshold uncertainty score0.895

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.310
Teacher spread0.288 · 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