Examining the Impact of Vulnerability and the Law of Justice on the IFRS Adoption Decision
Bibliographic record
Abstract
We investigate the impact of vulnerability and the law of justice indicators on the decision to adopt International Financial Reporting Standards (IFRS) by 133 countries. Applying robust Logit and Probit models to 2021 cross-sectional data, we find that the absence of corruption, state illegitimacy, a well-functioning civil justice system, and insufficient public services are helpful for IFRS adoption. On the other hand, results show that a country’s uneven economic development and human rights violations are detrimental to IFRS adoption. Our research confirms that requiring higher standards for financial and accounting reporting in the media, allocating sufficient budget amounts to support an equitable civil justice system, and coordinating efforts to reduce or eliminate economic inequality may help IFRS adoption. We argue that highlighting the positive benefits of IFRS adoption and the commensurate constructive policy outcomes may add the emphasis needed to convince governmental leaders to move toward IFRS adoption.
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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 | 0.018 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".