The Climatological Environmental Justice Index—Brazil, Canada, and Germany
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it