Policy brief as a knowledge transfer tool: to “make a splash”, your policy brief must first be read
Bibliographic record
Abstract
Since 2010, the research teams that we work with have produced dozens of policy briefs (PB) with the purpose of informing the various stakeholders of the results of our studies and their usefulness regarding public health practices, decision-making and policy change. Because they are only aids to decision-making, "A policy brief is just a piece of paper, it doesn't DO anything on its own", preparing these PBs should always form part of a broader knowledge transfer process. Therefore, they often serve as discussion tools during deliberative workshops focusing on the manner in which the results could be incorporated into practices and public policies. Based on these experiences, we have developed a guide for preparing policy briefs, which we have used with researchers over and over again in our training workshops. This training was offered in different formats lasting from three hours to two days. In this editorial, we use our different experiences to put forward a PB format intended for a non-scientific audience, to act as an influence on practices and policy-making.
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 | 0.037 | 0.184 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.016 | 0.018 |
| Insufficient payload (model declined to judge) | 0.024 | 0.020 |
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".