Whose social values? Evaluating Canada's ‘death of evidence’ controversy
Bibliographic record
Abstract
With twentieth- and twenty-first-century philosophy of science's unfolding acceptance of the nature of scientific inquiry being value-laden, the persistent worry has been that there are no means for legitimate negotiation of the social or non-epistemic values that enter into science. The rejection of the value-free ideal in science has thereby been coupled with the spectres of indiscriminate relativism and bias in scientific inquiry. I challenge this view in the context of recently expressed concerns regarding Canada's death of evidence controversy. The worry, raised by Stathis Psillos, is that as constructivist accounts of science demoted the previously secure status of evidence for drawing justified conclusions in science, we were left with no rational delineation between the right and wrong values for science. The implication for the death of evidence controversy is that we may have no rational grounds for claiming that the Canadian Government is wrong to interfere with scientific enterprise. But he does offer another avenue for reaching the conclusion that the wrong social values are directing the current stifling of some sectors of Canadian science. Psillos draws from standpoint epistemologies to devise a salient defence of ‘valuing evidence’ as a universalizable social value. That is, government bodies ought to enable scientific research via adequate funding as well as political non-interference. In this paper, I counter that (i) non-epistemic values can be rationally evaluated and that (ii) standpoint epistemology's universalizable standpoint provides an inadequate framework for negotiating social values in science. Regarding (i), I draw from the evidence-based medicine debate in philosophy of medicine and from feminist empiricist investigations into the science–values relationship in order to make the argument for empirically driven value arbitration. If social values can be rationally chosen in the context of justification, then we can have grounds for charging the Canadian leadership with being ‘at war with science’. (ii) I further argue that my recommended empiricist methodology is preferable to Psillos's search for universalizable perspectives for negotiating social values in science because the latter method permits little more than the trivial conclusion that evidence is valuable to science.
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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 | 0.056 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.039 | 0.044 |
| Scholarly communication | 0.040 | 0.012 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.021 | 0.019 |
| Insufficient payload (model declined to judge) | 0.004 | 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".