Adaptation or Manipulation? Unpacking Climate Change Response Strategies
Bibliographic record
Abstract
Adaptation is a key feature of sustainable social-ecological systems.As societies traverse various temporal and spatial scales, they are exposed to differing contexts and precursors for adaptation.A cursory view of the response to these differing contexts and precursors suggests the particular ability of persistent societies to adapt to changing circumstances.Yet a closer examination into the meaning of adaptation and its relationship to concepts of resilience, vulnerability, and sustainability illustrates that, in many cases, societies actually manipulate their social-ecological contexts rather than adapt to them.It could be argued that manipulative behaviors are a subset of a broader suite of adaptive behaviors; however, this paper suggests that manipulative behaviors have fundamentally different intentions and outcomes.Specifically, adaptive behaviors are respectful of the intrinsic integrity of social-ecological systems and change is directed toward internal or self-regulating modification.By way of contrast, manipulative behaviors tend to disregard the integrity of social-ecological systems and focus on external change or manipulating the broader system with the aim of making self-regulation unnecessary.It is argued that adaptive behaviors represent long-term strategies for building resilience, whereas manipulative behaviors represent short-term strategies with uncertain consequences for resilience, vulnerability, and the sustainability of social-ecological systems.Of greatest significance; however, is that manipulative strategies have the potential to avoid authentic experiences of system dynamics, obscure valuable learning opportunities, create adverse path dependencies, and lessen the likelihood of effective adaptation in future contexts.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".