Good thinking: six ways to bridge the gap between scientists and policy makers: Table 1
Bibliographic record
Abstract
In public health, it is desirable that scientists and policy makers communicate their knowledge effectively or run the risks of barriers in language and understanding. More incentives and opportunities to collaborate will help scientists and policy makers appreciate their different goals, career paths, attitudes towards information, and perception of time.1 2 Knowledge brokers can also bring scientists and policy makers closer to understanding each other and the contribution each can make to the other.1 2 Based on a consideration of three types of key players (scientists, policy makers, and knowledge brokers), each divided into two categories (content and people) (table 1), six ways are suggested to bridge the gap between scientists and policy makers. View this table: Table 1 Six ways to bridge the gap between scientists and policy makers 1. Convey science contents to policy makers Research outputs should be made accessible to policy makers.1 In some cases, complex analyses must be simplified3 and stepped …
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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 | 0.036 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.015 | 0.023 |
| Scholarly communication | 0.026 | 0.024 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.011 | 0.016 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".