Ethical Considerations for Sustainable Development: Third Annual Gilbert and Sarah Kerlin Lecture
Bibliographic record
Abstract
I would like, first of all, to express my thanks for this invitation to talk to you in this Kerlin Lecture about an ethics for sustainable development.I feel especially honored to be here, as I am not a lawyer, and certainly do not consider myself an ethicist.Nevertheless, ethics and sustainable development is a theme of growing importance in today's world, where globalization is too often given as the only way for solving our problems.It is also a theme with which I have a certain history, and it is this history that I would like to share with you.My work in relation to environmental issues began many years ago in Colombia, in a region known as the Sierra Nevada de Santa Marta.The indigenous communities that live there consider this region sacred territory.'I was fortunate to live and work for over 20 years with both the indigenous communities and the local peasant farmers.I worked especially with the Kogi community, 2 in a joint search for holistic solutions to their multiple
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".