Bibliographic record
Abstract
In what sense must global ethics be global? In one sense, it must deal with global issues. In another, it must not be parochial but inclusive of normative views from around the world. So far, global ethics has met the first standard much better than the second. Authors based in the global South contribute approximately 5% of the internationally published research on global ethics. With this in mind, the co-editors of this special issue sought to bring more perspectives, experiences, and authors from India into the international global ethics conversation, and so they launched the Indian Global Ethics Initiative. Their first step, this special issue, presents Indian experience and authors on topics including urban development, care ethics, women’s empowerment, fair trade, distorted policy research, poverty, and health. Much of this work is grounded by the authors’ experience in policy-making and advocacy for social and global justice. The co-editors invite contact from interested readers who would like to join and continue this Indian Global Ethics Initiative, as well as readers who would like to take similar initiatives in other regions.
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 | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.020 | 0.007 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.024 | 0.009 |
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".