The New Human Capital Disclosures in Form 10-Ks of Large and Small S&P 500 Companies
Bibliographic record
Abstract
Since 2020, the U.S. Securities and Exchange Commission has required a U.S.-listed company to describe its human capital (HC) management in Form 10-K to the extent such information is material to understanding its business. Instead of mandating the form or content of the disclosure or defining what HC is, the SEC relies on the registrants to use the principles-based approach in determining the details to be provided. The current research examined the HC disclosures of the 100 largest and 100 smallest S&P 500 companies to study the nature and level of the disclosures in their Form 10-Ks. The results showed that different companies disclosed different HC attributes under a variety of themes. There was a lack of quantitative details in most companies’ disclosures, and there was a significant disparity in the amount of information provided and the level of emphasis placed on the different HC attributes and themes by different companies. Company size was positively associated with some themes of HC disclosure but negatively associated with a few other themes.
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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 | 0.005 | 0.029 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".