Towards a Deeper Understanding of Intellectual Capital
Bibliographic record
Abstract
This paper takes its cue from a paper by Kianto and Cabrilo (2022) presented at ECKM 2022. In their paper they raise concerns both with the theoretic underpinnings of the theory of Intellectual Capital and the more specific need to consider the impacts on new technologies and work structures. In the existing literature it has been proposed that Intellectual Capital is composed of a variety of components which have often been addressed somewhat independently. It is important to both investigate the nature of these sub-components and recognize the extent to which they interact. Some key concerns with Intellectual Capital and its subcomponents are discussed including their valuation, which presents significant challenges to traditional approaches of valuation. Other notable concerns relate to the underlying conceptual structure for Intellectual Capital, which needs further study with respect to its general intelligibility, its explanatory value, and in the light of major technological changes and the phenomenon of digitization.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.025 |
| Scholarly communication | 0.015 | 0.043 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".