The Climatological Environmental Justice Index—Brazil, Canada, and Germany
Bibliographic record
Abstract
The perception of climate change impacts is strongly influenced by the underlying social realities. In order to develop a model for climate change adaptation policies, the CC-VISAGES project (Climate Change Inferred through Social Analysis, Geography, and Environmental Systems) developed a Climatological Environmental Justice Index (CEJI) based on a developed Human Stress Index (HSI) and the Temperature Humidity Index (THI). Through a geographical information system (GIS) representation of HSI, THI, and CEJI, a vulnerability ranking of all communities in Germany, Canada, and Brazil could be revealed. The variables have been selected and measured in a country comparable manner allowing to proportion communities between the different countries. The data have been gathered from the nomenclature of territorial units for statistics (NUTS) level 3 (community level). This article will show how HSI has been developed and combined with the THI in order to develop the CEJI. A list of the vulnerable areas in each country according to HIS, THI, and ECJI will be presented as the findings and discussed.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".