Towards a Deeper Understanding of Intellectual 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
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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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.022 |
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