Why history matters to planning: Climate change, colonialism & maladaptation
Bibliographic record
Abstract
Preparing for the future remains an enigma: Climate change is worsening and communities are already overwhelmed by disastrous impacts. Adaptation planning has the potential to prepare communities, yet adaptations are often maladaptive, having the unintended effect of increasing vulnerability. Maladaptation begets maladaptation, leaving communities trapped in maladaptive path dependencies (MPDs). Tracing maladaptation backward in history reveals how MPDs are deeply rooted in settler colonialism. This issue cannot be addressed by simply increasing adaptation efforts today. Exploring alternate paths may be the only means forward. Indigenous worldviews provide insight into ways of relating people and place beyond the colonial status quo, producing contextual, effective adaptations. Deep and personal biocultural relationships enable better understanding of complex socio-ecological systems, more accurate knowledge and, critically, adaptive learning. Currently, MPDs and extractive knowledge practices render adaptation co-management impossible: Indigenous Knowledge is appropriated to further development goals, erasing Indigenous Leadership and, in the process, hobbling adaptive learning. In this short perspective article we explore the temporal relationship of spatial planning, the impact of climate change and the urgent need for transformation. In particular, we showcase how in order to effectively address climate vulnerability, adaptation planning must first reconcile the historical roots of MPDs and ongoing Indigenous injustice. • Maladaptation is increasing vulnerability to climate change. • Historical decisions perpetuate maladaptation through maladaptive path dependencies (MPDs). • Indigenous worldviews predate MPDs formed during settler colonialism. • Biocultural relationships empower adaptive learning and resilient decision making. • Colonial institutions maintain MPDs by erasing Indigenous Knowledge and Leadership.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.010 | 0.024 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 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".