An intellectual capital evaluation approach in a government organization
Bibliographic record
Abstract
Purpose The purpose of this paper is to provide an assessment framework for evaluating the success of knowledge management (KM) initiatives in a government setting. Design/methodology/approach The approach used was to first conduct a brief review of the leading thinking on KM and intellectual capital (IC) measurement approaches. The selection process used to recommend the results‐based management assessment framework (RMAF) as the most appropriate measurement framework is then discussed together with the development of logic models for all KM objectives. Finally, the validation methodology used, a survey design and data collection methodology, is described. Findings The study finds that the RMAF framework proved to be a good fit for KM assessment in a government setting. Research limitations/implications The evaluation of KM and IC are necessarily organization‐specific. Further research is needed to report on the generalizability of this evaluation approach. Practical implications The KM evaluation approach proposed here helped the government organization translate its KM strategy into action and enhanced management of the KM program. The proposed evaluation approach will help ensure that each type of stakeholder receives assessment results in a form that is of greatest use to them. Originality/value While there are many KM and IC metrics described in the literature, there have been limited attempts to address the evaluation question from a more holistic perspective. This paper shows how quantitative and qualitative measures can be combined to better assess the success of KM initiatives in a systematic and concrete manner.
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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 | 0.032 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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 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".