Bibliographic record
Abstract
This interdisciplinary conference was exceptional in that the papers were individually excellent, and there was a thread weaving through many of the papers, especially those presented by ecological economists, concerning the inadequacy of neo-classical economics to analyze and provide solutions to environmental problems, that was related to my presentation. Although I am a philosopher, not an economist, I work in the applied ethics field of environmental ethics, and the thesis of my paper was that the requirement by the Canadian federal government that recommendations from Health Canada and Environment Canada to Cabinet and the Treasury Board for regulating toxic chemicals be supported with cost-benefit analyses undermines the application of the ethical principles that ground the Canadian Environmental Protection Act (1999). I presented the paper to try out my ideas in preparation for my sabbatical, during which I will attempt to deepen the analysis and strengthen the paper. As at many conferences, the time allotted for discussion was very short, and I had to work hard to glean ideas for improvement from the questions and comments received. The paper was well received (one person suggest that I start a blog similar to Don Brown’s on ethical aspects of climate change). However, I realized that my argument to show that cost-benefit analysis would work against the principle of pollution prevention was weak. One of the comments helped me to realize that I could strengthen it by discussing the assumptions behind the practice of calculating in monetary terms the benefit of saving a human life in order to show that the benefit of reducing, for example, a certain number of cancer deaths is not outweighed by the cost to industry. Also, of course, I am not an expert in cost-benefit analysis, and I learned from two comments how to state the implications of the practice of discounting for intergenerational equity more accurately. I plan to revise this paper and submit it for publication. It is part of a broader study of Canada’s Chemicals Management Plan, which will bring in problems with risk assessment as well as risk management. The broader study will also place more emphasis on a comparative approach than I was able to in this presentation.
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 | 0.057 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.043 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".