Classification of countries by selected accounting system for IFRS
Bibliographic record
Abstract
This document extends the classification of previous IFRS accounting systems to a wider range of 28 countries that have already adopted IFRS, plus the United States of America (USA). Based on the fact that differences in accounting practices can still be detected even in the era of IFRS, we seek to confirm this point of view by analyzing a wider range of important countries, namely non-European countries and those who do not adopt IFRS (USA). The results classify countries into three groups based on the similarity of their accounting methods: 1) Australia and New Zealand, 2) countries influenced by the United States (USA, Canada, Korea, Peru, Chile, Brazil, Kuwait, Turkey and Israel) and 3) South Africa, Oman and other European countries. This study contains a combination of alternative explanations for the existence of accounting options with other types of analysis. In particular, economic proximity to the United States provides important evidence of American influence on accounting practices
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".