Reconciling safe planetary targets and planetary justice: Why should social scientists engage with planetary targets?
Bibliographic record
Abstract
As human activity threatens to make the planet unsafe for humanity and other life forms, scholars are identifying planetary targets set at a safe distance from biophysical thresholds beyond which critical Earth systems may collapse. Yet despite the profound implications that both meeting and transgressing such targets may have for human wellbeing, including the potential for negative trade-offs, there is limited social science analysis that systematically considers the justice dimensions of such targets. Here we assess a range of views on planetary justice and present three arguments associated with why social scientists should engage with the scholarship on safe targets. We argue that complementing safe targets with just targets offers a fruitful approach for considering synergies and trade-offs between environmental and social aspirations and can inform inclusive deliberation on these important issues.
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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 | 0.039 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.079 |
| Scholarly communication | 0.016 | 0.026 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.014 | 0.012 |
| 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".