Bibliographic record
Abstract
Purpose The purpose of this paper is to address the knowledge management (KM) challenges faced by the administration of a small university which does not have a mature research culture. Design/methodology/approach The paper follows both technocratic as well as ecological approaches to develop a sustainable KM. Strengths, weaknesses, opportunities, and threats analysis has been used to assess the problem environment. Findings The paper investigates the main issues faced by a small university to enhance its research reputation and identifies key components of a KM system that can be established to achieve these objectives. Research limitations/implications KM is of paramount importance for most organizations and a university is no exception. Although knowledge is constantly being accumulated but it cannot be taken for granted. In the absence of a KM system to facilitate the growth and transfer of knowledge, knowledge base can easily be eroded. This is truer for universities engaged in imparting applied knowledge in business, trades, and technology‐related areas. Originality/value Most of the reported applications of KM in the education sector are limited to localized applications of information technology (IT). The present paper provides a comprehensive approach for institute‐wide KM by looking at the problem from both the ecological as well as the IT perspective, therefore, providing a more sustainable KM culture. There‐in lies the value of this paper.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".