Bibliographic record
Abstract
Abstract Editor's Summary In the medical field, knowledge translation is the process of putting research findings into action for patient education, practitioners' use and further research. It is a method of codifying what has been learned through research to improve communication within the professional community. The same view of knowledge sharing may be applied to library and information science (LIS). But the LIS community has been slow to adopt and implement our own research, to gain practical value by translating theory to knowledge. Greater attention should be paid to communication within the professional community to ensure the effective spread and use of knowledge gleaned from information science research. Just as medical informatics serves knowledge transfer in the field of medicine, exploring community informatics could shed light on how other disciplines, including LIS, translate knowledge to action through communication processes within professional communities.
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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 | 0.022 | 0.108 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.023 | 0.017 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.010 | 0.019 |
| Insufficient payload (model declined to judge) | 0.023 | 0.009 |
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".