The evolution of the intellectual capital concept and measurement
Bibliographic record
Abstract
This paper presents two dimensions of intellectual capital (IC): the concept itself and the measurement of IC. In the conceptual section, the importance of IC for competitive advantage and its evolution from practice to academia is discussed. The number and diversity of IC models is considered and their points in common are drawn out: namely, three categories, representing the individual, the collectivity and the relationship perspectives. The importance of social capital for the organization’s survival in the current economic environment is explained, a related bibliometric analysis is reported and an IC model acknowledging this component is suggested. The advent of new kinds of capital is explored and a perspective for their integration with the IC model is proposed. In the measurement section, the foundations of IC measurement and different metrics are discussed. A list of factors to be considered for the choice of the ideal set of metrics is presented. The Results-Based Management and Accountability Framework is explained and the evaluation of the Canadian Chemical, Biological, Radiological and Nuclear Research and Technology knowledge management initiative is given as an example. Recommendations to the reader on how to build their own assessment strategy are made and, in conclusion, future research venues are suggested.
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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 | 0.027 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.019 |
| Science and technology studies | 0.003 | 0.024 |
| Scholarly communication | 0.014 | 0.022 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".