Following the blind? Database coding policies and the case of <scp>IFRS</scp> noncompliance
Bibliographic record
Abstract
Abstract We present a case illustrating the pitfalls of insufficient disclosure of commercial databases' coding policies. We replicate the finding in the literature that a nontrivial percentage of firms mandated to adopt IFRS ignore this obligation. Specifically, Pownall and Wieczynska (2018, Contemporary Accounting Research , 35 (2), 1029–1066) report more than 3,000 cases, or 10% of all mandated firms in the European Union. When using primary data sources (applicable local regulations and firms' annual reports), we find that noncompliance with IFRS adoption is nonexistent in the one‐to‐one replication using the same firm‐year observations. We attribute the prior misperception to the commercial database's insufficient disclosure of a misleading coding policy of the consolidation item. We also show that no other data provider correctly captures consolidation status, which determines whether firms must report under IFRS. In response to this gap, we showcase the application of bidirectional encoder representations from transformers (BERT) models for extracting the consolidation status and offer guidance for coding IFRS‐mandated firms. Our article underscores the need to exercise caution when using secondary data sources.
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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 | 0.088 | 0.322 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".