Empirically derived guidance for social scientists to influence environmental policy
Bibliographic record
Abstract
Failure to stem trends of ecological disruption and associated loss of ecosystem services worldwide is partly due to the inadequate integration of the human dimension into environmental decision-making. Decision-makers need knowledge of the human dimension of resource systems and of the social consequences of decision-making if environmental management is to be effective and adaptive. Social scientists have a central role to play, but little guidance exists to help them influence decision-making processes. We distil 348 years of cumulative experience shared by 31 environmental experts across three continents into advice for social scientists seeking to increase their influence in the environmental policy arena. Results focus on the importance of process, engagement, empathy and acumen and reveal the importance of understanding and actively participating in policy processes through co-producing knowledge and building trust. The insights gained during this research might empower a science-driven cultural change in science-policy relations for the routine integration of the human dimension in environmental decision making; ultimately for an improved outlook for earth's ecosystems and the billions of people that depend on them.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".