Editorial: Knowledge management and e-learning: Improving the safety of technologies and devices
Bibliographic record
Abstract
Technology and device safety has emerged as an important area of research across many disciplines. In this issue of Knowledge Management and e-Learning there is as exploration of some of the key issues and considerations surrounding safety. The issue has a range of papers that can be organized into several themes around safety including the design of systems, their implementation, user perceptions, emerging roles and education. A number of methods and approaches are described that can improve safety. Such research is critical to advancing the evidence base and knowledge in safety science and is an essential aspect of foundational and iterative improvement of technology safety over time.
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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.025 | 0.023 |
| Insufficient payload (model declined to judge) | 0.021 | 0.018 |
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".