Engaging at the science-policy interface as an early-career researcher: experiences and perceptions in biodiversity and ecosystem services research
Bibliographic record
Abstract
Effective knowledge exchange at science-policy interfaces (SPIs) can foster evidence-informed policy-making through the integration of a wide range of knowledge inputs. This is especially crucial for conservation and sustainable use of biodiversity and ecosystem services (ES), human well-being and sustainable development. Early-career researchers (ECRs) can contribute significantly to knowledge exchange at SPIs. Recognizing that, several capacity building programs focused on sustainability have been introduced recently. However, little is known about the experiences and perceptions of ECRs in relation to SPIs. Our study focused on SPI engagement of ECRs who conduct research on biodiversity and ES, as perceived and experienced. Specifically, we addressed ‘motivations’, ‘barriers’ and ‘opportunities and ‘benefits’. A total of 145 ECRs have completed the survey. Our results showed that ECRs were generally interested to engage in SPIs and believed it to be beneficial in terms of contributing to societal change, understanding policy processes and career development. Respondents perceived lack of understanding about involvement channels, engagement opportunities, funding, training, perceived credibility of ECRs by other actors and encouragement of senior colleagues as barriers to engaging in SPIs. Those who have already participated in SPIs generally saw fewer barriers and more opportunities. A key reason for dissatisfaction with experience in SPIs was a lack of impact and uptake of science-policy outputs by policymakers – an issue that likely extends beyond ECRs and implies the need for transformations in knowledge exchange within SPIs. In conclusion, based on insights from our survey, we outline several opportunities for increased and better facilitation of ECR engagement in SPIs.
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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 | 0.031 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| 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".