Conceptualizing and contextualizing research and policy for links between climate change and migration
Bibliographic record
Abstract
Purpose – This paper aims to present a critical review of some literature on climate change and migration through conceptualizing and contextualizing the linkages between the two topics. Much literature on links between climate change and migration tends to downplay ambiguities in the terms and the limited empirical evidence. Conceptualizing refers to the knowledge gaps and the need to understand and detail (even if not agreeing on) conceptual issues such as terminology, definitions, linkages, drivers, thresholds, implications, data requirements and methodologies. Contextualizing refers to understanding the climate change and migration debate within wider topical and geographical contexts. Results identify major qualitative and quantitative gaps. Qualitatively, limited material exists on why people react differently to similar environmental stressors and why certain outcomes may arise. Quantitatively, credible and verifiable measures are not always available for assessing the climate change impacts on migration. This paper recommends a stratified, multi-disciplinary approach to facilitate policies regarding climate change and migration connections. Design/methodology/approach – Illustrative literature review, clustering important themes found in published research and policy documents. First, qualitative aspects are covered, particularly in terms of definitions and terminology. Second, quantitative aspects are detailed, particularly in terms of data available and estimates made. Further, the paper is organized around two distinct areas, i.e. conceptualizing and contextualizing climate change and migration links. Findings – Results identify major qualitative and quantitative gaps. Qualitatively, limited material exists on why people react differently to similar environmental stressors and why certain outcomes may arise. Quantitatively, credible and verifiable measures are not always available for assessing the climate change impacts on migration. This paper recommends a stratified, multi-disciplinary approach to facilitate policies regarding climate change and migration connections. Originality/value – Without being comprehensive in the literature covered, this paper provided a critical overview and synthesis of climate change and migration work through the lens of conceptualization and contextualization. Major gaps in the literature were identified through an illustrative, not complete, review. Qualitative and quantitative aspects were covered including definitions, terminology, data available and estimates being made.
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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 | 0.071 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.007 | 0.043 |
| Scholarly communication | 0.024 | 0.031 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.008 | 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".