The Equity Dimension of Climate Change: Perspectives From the Global North and South
Bibliographic record
Abstract
The articles in this thematic issue represent a variety of perspectives on the challenges for equity that are attributable to climate change. Contributions explore an emerging and important issue for communities in the Global North and Global South: the implications for urban social equity associated with the impacts caused by climate change. While much is known about the technical, policy, and financial tools and strategies that can be applied to mitigate or adapt to climate change in communities, we are only now thinking about who is affected by climate change, and how. Is it too little, too late? Or better now than never? The articles in this thematic issue demonstrate that the local impacts of climate change are experienced differently by socio-economic groups in communities. This is especially the case for the disadvantaged and marginalized—i.e., the poor, the very young, the aged, the disabled, and women. Ideally, climate action planning interventions should enhance quality of life, health and well-being, and sustainability, rather than exacerbate existing problems experienced by the disadvantaged. This is the challenge for planners and anyone working to adapt to climate change in our communities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.006 | 0.013 |
| 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".