Methods for collaboratively identifying research priorities and emerging issues in science and policy
Bibliographic record
Abstract
Summary 1. There is a widely recognized gap between the data generated by researchers and the information required by policy makers. In an effort to bridge the gap between conservation policy and science, we have convened in several countries multiple groups of policy makers, practitioners and researchers to identify priority information needs that can be met by new research in the social and natural sciences. 2. The exercises we have coordinated included identification of priority policy‐relevant research questions in specific geographies (UK, USA, Canada); questions relating to global conservation; questions relating to global agriculture; policy opportunities in the United Kingdom; and emerging global conservation issues or ‘horizon scanning’. 3. We outline the exercises and describe our methods, which are based on principles of inclusivity, openness and democracy. Methods to maximize inclusiveness and rigour in such exercises include solicitation of questions and priorities from an extensive community, online collation of material, repeated voting and engagement with policy networks to foster uptake and application of the results. 4. These methods are transferable to a wide range of policy or research areas within and beyond the conservation sciences.
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 | 0.258 | 0.407 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.008 | 0.027 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.037 | 0.007 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".