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Culture Matters: How Our Culture Affects the Audit*

2010· article· en· W1617411210 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueAccounting Perspectives · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicMilitary, Security, and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAuditProcess (computing)Public relationsBusinessInternational standardPolitical scienceAccountingComputer science

Abstract

fetched live from OpenAlex

Abstract If the influence of national cultures on the implementation of global standards is not taken into account, the result will be inconsistent implementation at best and outright failure at worst. The experiences in fields such as medicine, peacekeeping, aviation, and environmental protection offer insight into possible difficulties with the implementation, beginning in 2010, of International Standards on Auditing (ISAs) by members of the International Federation of Accountants. Some countries may have difficulty with implementation because of the differences between their cultural assumptions and those embodied in the standards to be adopted. It is too soon to know if and where that will happen, especially because the data on first experiences will not begin to be available until 2013. However, cultural‐comparison data can be used to foresee which countries may have difficulty with implementation. But if unintended consequences do become evident, it will be important not to assume that the standards and the standard‐setting process are defective; it is more likely that practitioners will need help in interpreting the ISAs in light of their local culture. A useful first step would be for standard‐setting bodies to identify explicitly the cultural assumptions inherent in the standards they produce. The standard setters can then give that information to those responsible for standards implementation at the practitioner level to help promote consistent application of the standards globally.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.564
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
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 head0.015
GPT teacher head0.316
Teacher spread0.301 · 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