Bibliographic record
Abstract
Knowledge repositories are increasingly being viewed as a special form of knowledge management in organizational memory information systems (OMISs). Presented in this paper are the design concepts and guidelines for building a knowledge repository, and its practical implementation in the form of a prototype. The needs and the organizational and technical challenges associated with the undertaking of such a project are identified, and recommendations and strategies for overcoming the restrictions are discussed. In light of these repository-building guiding principles, the prototype of such a knowledge management system is envisaged to be a Web-based electronic repository of online pedagogical resources, built to help foster a learning organization that works together to gather and share knowledge. As per the design guidelines, information resources within this prototype are combined with user insights and experience in the form of associated annotations and then categorized within a subject tree to appear as knowledge to the repository users.
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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.019 | 0.029 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".