Re-conceptualizing climate maladaptation: Complementing social-ecological interactions with relational socionatures
Why this work is in the frame
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Bibliographic record
Abstract
• Climate maladaptation is often studied using an “interactional ontology.” • “Relational ontologies” deepen the nature, causes, and redressal of maladaptation. • Maladaptation is constituted through dynamic material and discursive relations. • A case from Maharashtra, India demonstrates the necessity of relational approaches. Cases of climate maladaptation are increasingly documented. Its identification and redressal has become a priority for researchers and policymakers concerned with climate vulnerability reduction. The ability to address climate maladaptation hinges on being open to its diverse causes, manifestations, and impacts. This study argues that climate maladaptation analyses are dominated by an “interactional ontology”—the understanding that it can be explained as an observable outcome from how separate social, economic, and political systems interact in moments of time. Consequently, efforts to curb climate maladaptation often target the institutional contexts (e.g., rules, regulations) understood as enabling adaptation practices to aggravate climate risks. But this only captures a partial aspect of climate maladaptation, neglecting underlying causes and processes. We argue a “relational ontology” can complement the “why and how” of maladaptation. A relational ontology understands climate maladaptation as an evolving process constituted through dynamic material and discursive relations, versus an observable outcome from separately interacting systems. By analyzing how adaptation initiatives are related to, framed, and politicized, assembly processes are rendered visible. To demonstrate this, we study the Government of Maharashtra’s (India) Jalyukt Shivar Abhiyan , a program aimed at increasing water conservation to “free” 20,000 villages from drought impacts. From our theorization and empirical case, we discuss how a relational ontology contributes to debates in the climate maladaptation literature and invites approaches for mitigating this phenomenon.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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 it