MétaCan
Menu
Back to cohort
Record W4391543739 · doi:10.1016/j.bar.2024.101355

Auditing for fraud and corruption: A public-interest-based definition and analysis

2024· article· en· W4391543739 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

VenueThe British Accounting Review · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicCorruption and Economic Development
Canadian institutionsYork University
Fundersnot available
KeywordsAuditLanguage changePublic interestAccountingPolitical scienceBusinessLawPhilosophy

Abstract

fetched live from OpenAlex

To better understand how the practice of auditing can be more effectively enrolled in the fight against fraud and corruption, this study (1) examines how these problems are viewed and defined by the public and (2) contrasts this view and definition with that of professional auditors. The examination is informed by the dispositive theory of Foucault and an inductive analysis of a large (90,000+) multi-year sample of news stories related to fraud and corruption in the Italian health sector. While auditors define these problems in relatively narrow terms and consign them to ‘a form of risk, a threat to reputation and revenue, and a cost of doing business,’ the study finds that the public has a broader definition and a greater concern with problematic acts and actors ‘in and of themselves’. These findings have important implications for the audit expectations gap and how it might be addressed. The study also provides a useful analytical method for locating and better understanding fraud and corruption in other large, institutional settings.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.983
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0020.000
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 head0.090
GPT teacher head0.328
Teacher spread0.238 · 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