Toward Teaching Environmental Ethics: Exploring Problems in the Language of Evolving Social Values
Bibliographic record
Abstract
I explore the problems created by the natural and social science approaches to values in higher education, arguing that over time they will render moral language unintelligible. I illustrate these problems with an examination of the value implications of the Yukon Wolf Conservation and Management Plan. I suggest a way in which value education at the primary and secondary school lev-els could help prepare adults of the future for a kind of policy making that promotes the values stipulated in environmental law. I use the concept of environmental citizenship pioneered by Environment Canada coupled with training in traditional values in the context of a variety of fields in the arts, humanities, and the sciences. Résumé J’analyse les problèmes créés par les approches scientifiques naturelles et sociales aux valeurs de l’éducation supérieure, en alléguant qu’ils rendront le langage moral inintelligible avec le temps. J’illustre ces problèmes avec un examen des implications des valeurs véhiculées dans la politique de gestion du loup du Yukon. Je suggère comment l’éducation aux valeurs au primaire et au secondaire pourrait aider à préparer les adultes de demain à une formulation des politiques qui fait la promotion des valeurs énoncées dans la législation environnementale. J’utilise le concept d’écocivisme lancé par Environnement Canada jumelé à une formation aux valeurs traditionnelles dans le contexte d’une variété de champs dans les arts, les sciences humaines et les sciences.
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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 | 0.013 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.066 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".