Taxonomic Chaos in the Confused Canadian Bioethics Industry: Apres Moi la Deluge
Bibliographic record
Abstract
This paper presents a conceptual analysis of a decade-old movement in Canada to purportedly raise ethical standards in research with human subjects, even though no systematic evidence has ever been presented either that there are serious ethical problems (especially in psychological research), or that the solutions imposed by the movement would improve the ethical situation, and not harm research's fundamental epistemological enterprise. The movement began with the activities of a committee from Canada's three major government research councils, the Tri-Council Committee (TCC). Like all ideological enterprises, it provided taxonomic chaos by, for example, confusing ethics with epistemology and feelings of discomfort concerning an area of investigation with intellectual expertise about that area. It also went beyond its American counterparts by calling its proposals a code of conduct rather than guidelines, and proposing that if a so-called research participant (i.e., a subject) did not like the investigator's hypotheses, she or he could withdraw “her” or “his” data. Even after the TCC and its various bureaucratic progeny retreated (though ambiguously) from these absurd positions, there has been a maintenance of such positions as the right and responsibility of IRBs to advise not only on the ethical issue of the treatment of subjects, but also on epistemological issues of research design. These issues require not only expertise in the requisite disciplines, but also an intimate familiarity with highly specialized sub-areas. In practical terms senior researchers may be able deal with the burgeoning North American bioethics industry and ignore the anti-epistemological and implicit principles according to which the industry operates. Younger researchers, who have no memory of how research used to be conducted, will succumb, and, in an epistemological sense, be “corrupted”. As the last phrase of my title suggests, senior researchers are currently acting like France's Louis XV.
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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 | 0.023 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.049 | 0.078 |
| Scholarly communication | 0.024 | 0.012 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".