Neoliberalism, Climate Change, and Displaced and Homeless Populations: Exploring Interactions Through Case Studies
Bibliographic record
Abstract
There is a growing attention to neoliberal policies and practices as they relate to climate change and housing within academic literature. However, the combined effects of neoliberal political and economic decisions on the interaction between climate change and displaced and homeless populations have not been substantially explored. In this paper, we identify and focus on three key re-emerging themes prevalent within neoliberal discourses: economic considerations, individualization, and short-termism. To examine the intersecting influence of climate change and these themes on vulnerable populations, the following case studies are discussed: displaced populations in the Middle East and North Africa (MENA) region, refugees in Kenya, and tiny homes programs in the U.S. and Canada. The diversified contexts and levels of analysis allow for more nuanced understanding of the variety of ways in which neoliberal influences and climate-induced events impact the most vulnerable populations. We argue for the need to change the framing of these issues, which are often presented in neoliberal terms and are driven by neoliberal logic. We then present potential avenues for resolving the identified issues, such as through systemic changes, development of long-term solutions, and focusing on community-based adaptation (CBA) programs.
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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 | 0.017 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.021 | 0.019 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".