Bibliographic record
Abstract
September 09 2016 Science, Policy, Values: Exploring the Nexus Heather E. Douglas Heather E. Douglas Search for other works by this author on: This Site Google Scholar Author and Article Information Heather E. Douglas My thanks to Rachel Ankeny and the SPSP 2013 organizing committee for inviting me to put together the workshop that led to this collection of papers. The assistance of the Institute for the History and Philosophy of Science and Technology at the University of Toronto (especially Muna Salloum, Marga Vicedo, and Denis Walsh) and of Vicki Brett at the University of Waterloo was essential for making the workshop run properly. Thanks as well to the additional speakers who contributed so much to the discussion (Sergio Sismondo, Kieran O'Doherty, and Marc Saner) and to the 70 attendees who made it a vibrant day. Thanks to the anonymous peer reviewers who helped strengthen each of the papers, to Dylon McChesney for his invaluable editorial assistance, and to Ted Richards for his ongoing support. Finally, thanks to the authors for their hard work, perseverance, and insight. My hope is that this collection will spur greater engagement with the normatively complex terrain where science, policy, and values meet. Online Issn: 1530-9274 Print Issn: 1063-6145 ©2016 by The Massachusetts Institute of Technology2016MIT Press Perspectives on Science (2016) 24 (5): 475–480. https://doi.org/10.1162/POSC_e_00219 Cite Icon Cite Permissions Share Icon Share Facebook Twitter LinkedIn MailTo Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Search Site Citation Heather E. Douglas; Science, Policy, Values: Exploring the Nexus. Perspectives on Science 2016; 24 (5): 475–480. doi: https://doi.org/10.1162/POSC_e_00219 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll JournalsPerspectives on Science Search Advanced Search The importance of science for guiding policy decisions has been an increasingly central feature of policy-making for much of the past century. But which science we have available to us and what counts as adequate science for policy-making shapes substantially the specific impact science has on policy decisions. Policy influences which science we pursue and how we pursue it in practice, as well as how science ultimately informs policy. Values inform our choices in these areas, as values shape the research agendas scientists pursue, the issues debated as we decide on policy, and what counts as sufficient warrant in any given case. And what we value is shaped by our empirical understanding of what is, what is possible, and what is feasible. The interrelationships between values (what we care about), policy (how our institutions and practices are structured), and science (our best source of empirical knowledge) requires careful philosophical attention.... You do not currently have access to this content.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.007 | 0.025 |
| Scholarly communication | 0.022 | 0.019 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.038 | 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".