Environmental displacement: the common ground of climate change, extraction and conservation
Bibliographic record
Abstract
In this introduction to a special section on environmental displacement, we introduce the concept and ground it in seemingly distinct processes of climate change, extraction, and conservation. We understand environmental displacement as a process by which communities find the land they occupy irrevocably altered in ways that foreclose or otherwise impede possibilities for habitation or else disrupt access to resources within these spaces of life, work and socio‐cultural reproduction. Such dislocation amounts to environmental displacement on the grounds that it is justified by environmental or ecological rationales, motivated by desires to access natural resources, or else provoked by human‐induced environmental change and attempts to address it. Building from here, we make the case for why climate change and efforts to mitigate and adapt to it, extractive industries, and conservation initiatives should be analysed together as displacement inducing phenomena, as they are empirically connected in consequential ways and materialise from similar logics. We additionally lay out the contributions of the individual articles of the special issue and draw connections across them to help provide a preliminary framework for thinking through environmental displacement, including its causes, logics, and consequences, especially for vulnerable populations.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".