“No comment”: Language frictions and the <scp>IASB</scp> 's due process
Bibliographic record
Abstract
Abstract The IASB asserts that global stakeholder participation in the standard‐setting process is critical for developing and maintaining high‐quality accounting standards. However, the myriad languages used in countries that apply IFRS may impede this participation. We find that the IASB is less likely to receive comment letters from stakeholders in countries with languages that are linguistically distant from English. We also find that comment letters from more linguistically distant stakeholders are less likely to be quoted in IASB staff‐prepared comment letter summaries, suggesting that they have less influence in the redeliberation process. Path analyses show that this result arises from language frictions being associated with reduced writing quality and originality. We also find that language frictions prevent participation in other standard‐setting communication channels. Collectively, language frictions appear to impede the IASB's efforts to equitably obtain and consider valuable global feedback.
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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 | 0.095 | 0.424 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".