Commentary: Social-Ethical Values Issues in the Political Public Square: Principles vs. Packages
Bibliographic record
Abstract
This article explores decision making about social-ethical values issues by members of the public in the context of the recent Canadian federal election, held in late June 2004. All of these issues are sensitive and controversial, and I hesitated to address them in an article that I dedicate, with respect and admiration, to my friend and fellow medical lawyer-ethicist, Bernard Dickens. Over the years Bernie and I have discussed, debated and disagreed on many of them. It speaks to his tolerance, reasonableness and wisdom that those occasions were for me always ones of learning and respect, colored by his inimitable sense of humor. I hope that Bernie feels that, in some small measure, this article reflects those same characteristics, ones that he has modeled for so many of us over the years of his distinguished career.
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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 | 0.017 | 0.106 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.016 | 0.018 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.010 | 0.005 |
| Research integrity | 0.090 | 0.091 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".