Advancing a transformative social contract for the environmental sciences: From public engagement to justice
Bibliographic record
Abstract
Abstract Taking as a starting point Jane Lubchenco’s call for a renewed social contract for environmental science, this paper advances a framework for science’s place in society in which justice is central. A social contract is a desired vision of social order that distributes rights, responsibilities, and obligations among political actors. The magnitude of global ecological change, our collective inability to address ecological crises, and populist challenges to science have renewed interest in debates about existing social contracts with science. While Lubchenco’s vision of a social contract focuses on practical ways to improve the engagement of scientists with decision-makers and citizens, we argue that to achieve the objectives laid out by Lubchenco, justice—encompassing representation, distribution, and recognition—must be at the core of science-society relations. A justice-centred social contract with science requires acknowledgement on the part of scientists, administrators, decision-makers, and citizens of the biases, inequalities and inequities contained within and advanced by academic institutions. Orienting science towards justice provides a starting point for a more diverse, inclusive, and equitable culture of publicly-funded research.
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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 | 0.080 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.017 | 0.111 |
| Scholarly communication | 0.026 | 0.026 |
| Open science | 0.003 | 0.028 |
| Research integrity | 0.019 | 0.021 |
| Insufficient payload (model declined to judge) | 0.008 | 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".