Values, Ethics and Sustainability from an Environmental Justice Perspective
Bibliographic record
Abstract
ISEE-0926 Abstract: The problem of failing to ask “in whose best interests” we are working results in an accumulation of harms that add to environmental deficits. These deficits serve to extend the gap between the rich and the poor and thus work against attainment of the Millennium Development Goals (MDGs). At the 2005 ISEE conference in Johannesburg, South Africa, the theme of that meeting was on reducing the gap by bridging the interests of the “north” and the “south” under the theme “Sustaining World Health Through Environmental Epidemiology: setting a new global research agenda”. It resulted in a paper published in 2007 in the Epidemiology and Society section of Epidemiology entitled “Toward a global agenda for research in environmental epidemiology” (Vol. 18(1):162–166). This presentation focuses on the recommendations contained in that paper as they relate to environmental justice issues around biofuels and food security, specifically in terms of new transportation and energy policies. Our role in researching related questions is shown to influence the evidence base for maintaining the status quo or for embracing a new green agenda.
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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.052 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".