A framework for prioritization of intellectual capital indicators in R&D
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
Purpose The purpose of this paper is to present a framework for prioritizing intellectual capital (IC) indicators, as well as suggesting key improvement areas by using the Delphi approach and analytic hierarchy process (AHP) analysis. Design/methodology/approach The effectiveness of the framework is demonstrated through the case study of a public sector research and development organization. Findings This paper identifies three major characteristics of the framework: weighing indicators that should be based on an organization's strategies and contexts; employing multiple processes (Delphi and AHP approaches) which can overcome the limitation of a single methodology; and providing a visual map that can help management identify which indicators and related activities need attention and should be improved. Originality/value This research contributes to the literature and practices in several ways. First, this paper provides a practical and operational guideline on how to engage in IC management efficiently. Second, the authors try to integrate IC management into traditional management tools (e.g. quality management) by employing the concept of an operational feedback process and three screening processes. Third, this paper tests the possibility of using a Delphi approach in prioritizing IC indicators.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it