Pathways of influence: understanding the impact of philosophy of science in scientific domains
Bibliographic record
Abstract
Philosophy of science has the potential to enhance scientific practice, science policy, and science education; moreover, recent research indicates that many philosophers of science think we ought to increase the broader impacts of our work.Yet, there is little to no empirical data on how we are supposed to have an impact.To address this problem, our research team interviewed 35 philosophers of science regarding the impact of their work in science-related domains.We found that face-to-face engagement with scientists and other stakeholders was one of the most-if not the mosteffective pathways to impact.Yet, working with non-philosophers and disseminating research outside philosophical venues is not what philosophers are typically trained or incentivized to do.Thus, there is a troublesome tension between the activities that are likely to lead to broader uptake of one's work and those that are traditionally encouraged and rewarded in philosophy (and which are therefore the most consequential for careers in philosophy).We suggest several ways that philosophers of science, either as individuals or as a community, can navigate these tensions.
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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 | 0.012 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".