Governance and everyday adaptations? Examining the disconnect between planned and autonomous adaptation through justice outcomes
Bibliographic record
Abstract
Much of the current attention in research has focused on planned adaptation, i.e., public policy, but this overlooks the fact that human and societal responses to changes in the climate are ubiquitous. Thus, autonomous adaptation, the so-called everyday adaptation, continues to be largely unaccounted for. This obscures the understanding to what extent autonomous and planned adaptation are synergistic or conflicting, resulting in maladaptive, unjust, and unequal outcomes. We approach adaptation as a commons issue and integrate existing frameworks and concepts to show how planned and autonomous adaptation can be understood together to break down the dichotomy. This integrated approach, combined with a focus on the outcome of actions through the dimensions of climate justice, can support understanding of the actions and institutions that support equality and justice. We draw on examples from recent studies on everyday adaptations by farmers and urban dwellers in light of the framework.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.033 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".