Leveraging intellectual capital through product and process management of human capital
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 it