Leveraging intellectual capital through product and process management of human capital
Bibliographic record
Abstract
The current framework of intellectual capital is examined. It is argued that transformation of human capital into structural capital is counter‐productive for certain types of highly tacit, experiential and intuitive knowledge. In fact, the very process of structuralizing intellectual capital may institutionalize knowledge stocks and create core rigidities or result in the “false recipe” syndrome. An important understanding is that intellectual capital does not have to be explicitly owned by the firm in order to be valuable to it. Attempts to measure all aspects of intellectual capital may be counter‐productive and neglect the actual management of these intellectual capital assets towards a higher real firm valuation. Ultimately, a strategy for determining what knowledge to structuralize and manage as product and what knowledge not to structuralize and manage as process is necessary for a practical and profitable means of developing value in the concept of intellectual capital.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".