Collaboration and engagement produce more actionable science: quantitatively analyzing uptake of fish tracking studies
Bibliographic record
Abstract
Aquatic telemetry technology generates new knowledge about the underwater world that can inform decision-making processes and thus can improve conservation and natural resource management. Still, there is lack of evidence on how telemetry-derived knowledge can or has informed management, and what factors facilitate or deter its use. We present one of the first quantitative studies related to the science-action gap and evaluate factors that influence the uptake of fish telemetry findings into policies and practices, as well as social acceptance of these findings. We globally surveyed 212 fish telemetry researchers regarding the knowledge uptake of an applied fish telemetry research project of their choice. Respondents' personal and professional attributes, as well as the attributes of their chosen projects, were analyzed using machine learning algorithms to identify important factors that influenced the uptake (i.e., use, trust, and/or acceptance) of their findings. Researchers with extensive collaborations and who spent more time engaging in public outreach experienced greater uptake of their findings. Respondents with greater telemetry experience and commitment (e.g., more telemetry publications, higher proportion of research on fish telemetry) tended to achieve more social acceptance of their findings. Projects led by researchers who were highly involved and familiar with the fisheries management processes, and those where greater effort was devoted to research dissemination, also tended to experience greater uptake. Last, the levels of complexity and controversy of the issue addressed by the research project had a positive influence on the uptake of findings. The empirical results of this study support recent messages in the science practitioner literature for greater collaboration, knowledge co-production with partners, and public engagement to enable the transfer of knowledge and the use of evidence in decision-making and policies. Scientific organizations should consider shifting reward incentives to promote engagement and collaboration with non-scientific actors, and perhaps even rethinking hiring practices to consider personal and professional characteristics or attitudes such as altruism and networking skills given the influence of these factors in our model. Last, networks composed of both research and practice potentially have a key role in brokering and facilitating knowledge exchange and actions.
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 | 0.073 | 0.405 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".