Migration, Climate, and Education: Proposing Human Rights-Based Education for Internally Displaced Learners in Lower- and Middle-Income Countries
Bibliographic record
Abstract
The growing impacts of climate change are forcing families in low- and middle-income countries to migrate to urban areas, resulting in widespread internal displacement. Despite the significant disruptions this causes to children’s education, its educational consequences remain underexplored in climate change research. This study addresses the gap by adopting a Human rights-based approach (HRBA) to education and integrating insights from the Education in Emergencies framework while examining the impact of climate-induced displacement on education. Through a literature review of academic and policy documents, the research examines educational vulnerabilities of internally climate-displaced learners, including restricted access to schooling, declines in academic performance, and difficulties adapting to new learning environments. The challenges are pronounced for girls, reinforcing pre-existing gender disparities in education. Based on the findings, the study proposes targeted policy interventions, including climate-responsive education frameworks and economic protection measures for affected households.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".