An Evolving Model of Knowledge Management in Education and the South African Reality
Bibliographic record
Abstract
Concepts of Knowledge Management struck three educators in Gauteng, South Africa, and inspired them to devise a Knowledge Management model for education and schools. The model is focused on how Knowledge Management is sandwiched between the country’s educational policies and the bedrock of literacy and reading. It encompasses the Constitution, common value systems, common leadership/management skills and professional values, inherited language skills and cultural knowledge and lifestyles, general knowledge, information/literacy/digital/IT skills, Intellectual Capital and collaboration between educational entities, school librarians and colleagues, communities and stakeholders. The model highlights the many challenges existing in South African education and further inspired the three educators to consider their own achievements as School Librarians – one at a Public/Government High School and the other at a Public/Government Primary School. The High School Librarian has used IT to promote reading to great effect, while the Primary School Librarian has made important strides in helping her subject/learning area colleagues to teach Information Literacy.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.021 |
| Scholarly communication | 0.017 | 0.022 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".