Annual report IC disclosures in The Netherlands, France and Germany
Bibliographic record
Abstract
Purpose This paper replicates and extends the Bontis research on intellectual capital (IC) disclosures in Canadian companies and also elaborates on the Beaulieu et al. research on disclosures by Swedish firms. Design/methodology/approach The paper studies IC disclosures by French CAC‐40, Dutch AEX and German XETRA‐DAX publicly‐listed companies for the years 2000 and 2001. The paper also discusses country‐specific arguments in favour of and against voluntary disclosure by such companies and searches both the annual reports and financial statements for IC hits. Findings Applying the Gray‐scale to categorise countries, the paper finds not only that voluntary IC disclosure significantly differs between these countries, but also that this difference can be explained by country‐specific regulation and auditor conservatism. Research limitations/implications The paper only studies Dutch, French and German IC disclosures in annual reports and financial statements. These three countries are European Union member states but “differ” significantly from one another. The differences discussed in this paper, however, are by no means exhaustive, nor do they picture the “European situation” in full. Practical implications The paper recognises that the intangible nature of IC creates tension with current country‐specific legislation and strongly calls for convergence of applicable accounting standards and practices because of the increasing importance of IC and because of the improvement of corporate governance and policy making. Originality/value The paper not only extends (or fine‐tunes) previous research, but also links with the literature that discusses the consequences of country‐specific characteristics for accounting standards and 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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".