Bibliographic record
Abstract
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".