Bibliographic record
Abstract
Purpose Building on an integration of strategic human resource capital management and human capital disclosure literature streams, this paper explores the associations between human resource performance and human resource disclosure in the financial services sector. Design/methodology/approach Using content analysis and panel regression methods, the paper examines the extent, nature, and information content of human capital disclosures in the financial services sectors in North America during the global financial crisis period. Findings Labor costs and marginal labor productivity are significantly associated with human resource disclosure and the latter is significantly related to both financial (explicit) and non-financial (implicit or relational) components of the employment relationship. Results show inverted effects between the US and Canadian samples. The findings support a contingency view or “best-fit” approach to human resource capital management. Practical implications Differences in labor market structures and human capital attributes could have significant impacts on human capital disclosure strategies. More transparent and detailed disclosures regarding human resource capital management may provide useful and relevant information for investors and stakeholders in general. Originality/value The study provides insights into how labor market structures and human capital attributes jointly affect the extent and nature of corporate disclosures with regards to rents distribution and relational governance between employers and employees.
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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 | 0.007 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| 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".