Bibliographic record
Abstract
This chapter extends the earlier study of Bontis (2001) by critically reviewing the existing methods to measure and report intellectual capital. Bontis's (2001) study contributed significantly to the intellectual capital measurement and reporting literature. However, despite the growth in the field of IC and development and introduction of several new approaches to measure and report intellectual capital, no recent study has synthesized the IC measurement and reporting models. The objective of this chapter is to fill this gap in the literature by providing a critical review of 28 IC measurement models. To achieve this objective, the author partially adopts Sveiby's (2007) suggested classification scheme for categorizing the existing measurement models. The classification will enable the reader to uncover the common attributes of each model and to contrast the dissimilarities.
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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 | 0.017 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".