Determinants of intellectual capital disclosure in initial public offerings: case of Canadian firms
Bibliographic record
Abstract
The purpose of this paper is to investigate the determinants of intellectual capital information’s of firms that went through IPO. Our sample includes 43 firms that IPOs listed in the Toronto Stock Exchange in 2012 of which the prospectuses for the initial public offering are available. Our study, unlike other studies focuses on the issuing prospectuses. The paper applied a disclosure index comprising of 78 items (Bukh and al (2005)) to quantify the amount of information regarding intellectual capital included in the IPO prospectuses of canadian firms. Multiple regression model and Correlation is used. The results revealed that the managerial ownership, the presence of an audit committee and industry are significantly associated with the voluntary disclosure of information about the intellectual capital in prospectuses. While firm size, age, the audit committee’ activity and audit quality do not affect disclosure. The results are interpreted in the light of the increasing importance of disclosing information on intellectual capital to the capital market a in case of IPO and constitute a contribution to the ongoing debate on corporate reporting 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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".