Activities and Outputs of a Clinical Faculty: an Intellectual Capital Concept Map
Bibliographic record
Abstract
Abstract: The concept of intellectual capital (IC) was used to evaluate the activities and outputs of a university medical department. First, a conceptual framework was developed to highlight the importance of various activities as dimensions of IC. The conceptualization of IC was further developed using concept mapping (CM). The authors first considered the problem of what comprises IC and determined whether previous researchers have defined IC in terms of activities. The importance of IC, its definition as an organizational resource and activity, the link between IC and value creation and extraction activities, and the problem of the associated composition of IC taken from existing European guidelines and regulations were discussed. To begin to construct a classification of activities and outputs, the information currently employed for assessing the research, education, and related academic activities and outputs of faculty members were analyzed. Four different evaluation approaches were compared to identify the activities and outputs of a university medical department, and to consolidate the information being collected for evaluation of universities, university-affiliated research institutes, researchers within universities, and faculty within university departments into an inclusive set of activities and outputs. These were two forms of IC reporting, one used in Austrian universities and the other at a university-affiliated Swedish research institute together with two other long-established means of assessing faculty, the Research Assessment Exercise in the UK, and the faculty evaluation and promotion requirements at the University of Toronto in
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".