LIS professionals and knowledge management: some recent perspectives
Bibliographic record
Abstract
Purpose To identify the general perspectives of library and information science professionals on knowledge management and examine their assessments of its potential values, benefits, opportunities and threats to the profession. Design/methodology/approach An international survey was conducted using a web‐based questionnaire. The questionnaire targeted LIS professionals around the world, through the use of the IFLA‐L, KMDG‐L mailing lists. Findings The survey found an increased awareness among LIS professionals of their potential contribution to knowledge management, with a high agreement on its positive implications for both individuals and the profession. Research limitations/implications Although the survey was distributed through international mailing lists, it succeeded mainly in obtaining responses from Australia and New Zealand, the USA, the UK, South Africa and Canada. Thus, the findings may have limitations in their generalizability. Originality/value Knowledge management is a field with which the LIS community is already familiar. Despite its wide impact on many aspects of the profession, the wider ramifications of the relationship between the two as yet remain unclear. The paper attempts to contribute to further understanding of these ramifications.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".