Bibliographic record
Abstract
Global warming is causing more frequent and severe extremes in weather. Sea-levels are rising with island states poised to vanish. Over time, these trends are expected to worsen. If some predictions materialize, a sixth major extinction awaits us. What could be more important for environmental epidemiologists than to help inform policies that might avert such catastrophic harms? En route to possible extinction, as global average heating continues, local habitats will change thereby exposing local populations to environmental harms through exposure to challenges not before seen in such communities. Studying potential harms is prerequisite to health planning. Despite the ethical imperative to pursue research to help with adaptation, access to funds for research is less likely in a context of ideologically driven policy that denies climate change. Countries contributing both to denial and to greenhouse gas emissions are the more affluent in the world where wealth allows them a buffer against extremes in weather. Meanwhile, the poorer countries, not contributing to the accumulation of emissions, do not have buffering capacity and are suffering the greatest negative impacts from weather changes. These phenomena are leading to a widening of the 90:10 gap where 90% of the world’s research funding goes into the study of problems affecting only 10% of the global population. This session will reflect on the Polluter Pays and the Post-Cautionary Principles. The hardships resulting in the countries where land is being bought by rich countries with the sole ambition of feeding their local populations will also be addressed. The presentation aims to engage the audience and provide the opportunity for discussion by identifying, among others, the principles noted above in each of this session’s presented papers.
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 | 0.017 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.017 | 0.034 |
| Scholarly communication | 0.020 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.017 | 0.037 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".