Challenges Turning Environment and Sustainability Science Into Policy
Bibliographic record
Abstract
In theory, there is a strong, two-way relationship between sustainability research and public policy that functions in synchrony to identify, understand, and ultimately address ecological problems for the greater good of society. In reality, such a cooperative relationship is rarely found. Instead, researchers and policymakers face a suite of challenges that prevent effective communication and collaborative pursuits, prolonging the period required to address environmental issues. In this chapter, the authors apply a novel interdisciplinary approach to identify key barriers and solutions to translating research into policy. In doing so, the authors present two separate discussions focused on the natural and social sciences. The authors also review established research-to-policy frameworks to develop the new “cohesive” framework. By addressing key barriers between researchers and policymakers, society will be better able to respond to the various environmental stressors that it faces today.
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 | 0.019 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.028 |
| Scholarly communication | 0.024 | 0.032 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.015 | 0.022 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".