A Social-Pluralistic View of Science Advising
Bibliographic record
Abstract
In this dissertation, I bring together two disciplines: Science, Technology, and Society studies and the Philosophy of Science, to develop a social-pluralistic account of science advising. I use three prominent theorists in the philosophy of science to critique three prominent views in the science, technology, and society field relating to science advising. I argue that the science, technology, and society literature does not fully account for the value-ladeness of scientific research. To that end, I develop a social-pluralistic account of science advising: social, because advice should come from panels or institutions rather than individuals, and pluralistic, because we should assess the credibility of advice along several dimensions of objectivity. I then apply my view to two real world examples: first, an EPA report on the harmful effects of environmental tobacco smoke, which faced lawsuits from the tobacco industry, and second, the Government of Canada’s use of Roundup Ready canola, a biotechnology, as a “value neutral” policy response to avoid discussions about the socio-cultural impact of industrial agriculture. These examples help to demonstrate the usefulness of my view in responding to real-world situations. A social-pluralistic view of science advising helps ensure that the role of values in producing scientific knowledge and science advice are legitimate, helps ensure that diverse viewpoints are actively considered as part of the advisory process, and ensures that the resulting advice is independent of any one person’s views, beliefs, or values.
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 | 0.027 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.068 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".