Bibliographic record
Abstract
In the Chronicles of Narnia series by C.S. Lewis, Aslan the all-powerful but benevolent lion does not need to have his tail twisted; rather, he twists tails to create convergence and harmony in his dream world. In this issue's lead article, "Twisting the Lion's Tail: Collaborative Health Policy Making in British Columbia," the authors discuss the problems regarding better coordination of health services research, knowledge translation and policy making. The roles of academia, health authorities and government are presently unclear, with leadership differences, power discrepancies, conflicting agendas, lag times and systemic structural complexity. Exploring these issues in British Columbia, Lindstrom, MacLeod and Levy advocate a change in perspective from practice gaps to bridging knowledge boundaries. Recommendations include networking of academia, action research and strengthening of relationships between stakeholders. However, a key cohesive element seems missing. Health technology assessment (HTA) is a formidable, dynamic driving force. With over 20 years' experience in HTA, Canada has a number of world-class innovative agencies federally and provincially that actively involve academia to generate evidence for informed policy making. Increased use of evidence-based medicine in research and the clinic may be achieved by augmenting HTA's scientific capacity through the creation of pan-Canadian exchange forums and by boosting the demand for knowledge translation.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.031 | 0.015 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.007 | 0.024 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".