A review and meta-analysis of collaborative research prioritization studies in ecology, biodiversity conservation and environmental science
Bibliographic record
Abstract
Collaborative research prioritization (CRP) studies have become increasingly popular during the last decade. By bringing together a diverse group of stakeholders, and using a democratic process to create a list of research priorities, these methods purport to identify research topics that will better meet the needs of science users. Here, we review 41 CRP studies in the fields of ecology, biodiversity conservation and environmental science that collectively identify 2031 research priorities. We demonstrate that climate change, ecosystem services and protected areas are common terms found in the research priorities of many CRP studies, and that identified research priorities have become less unique over time. In addition, we show that there is a considerable variation in the size and composition of the groups involved in CRP studies, and that at least one aspect of the identified research priorities (lexical diversity) is related to the size of the CRP group. Although some CRP studies have been highly cited, the evidence that CRP studies have directly motivated research is weak, perhaps because most CRP studies have not directly involved organizations that fund science. We suggest that the most important impact of CRP studies may lie in their ability to connect individuals across sectors and help to build diverse communities of practice around important issues at the science-policy interface.
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 | 0.025 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.017 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".