Philosophy in Science: Can Philosophers of Science Permeate through Science and Produce Scientific Knowledge?
Bibliographic record
Abstract
Most philosophers of science do philosophy ‘on’ science. By contrast, others do philosophy ‘in’ science (PinS), that is, they use philosophical tools to address scientific problems and to provide scientifically useful proposals. Here, we consider the evidence in favour of a trend of this nature. We proceed in two stages. First, we identify relevant authors and articles empirically with bibliometric tools, given that PinS would be likely to infiltrate science and thus to be published in scientific journals (‘intervention’), cited in scientific journals (‘visibility’), and sometimes recognized as a scientific result by scientists (‘contribution’). We show that many central figures in philosophy of science have been involved in PinS, and that some philosophers have even ‘specialized’ in this practice. Second, we propose a conceptual definition of PinS as a process involving three conditions (raising a scientific problem, using philosophical tools to address it, and making a scientific proposal), and we ask whether the articles identified at the first stage fulfil all these conditions. We show that PinS is a distinctive, quantitatively substantial trend within philosophy of science, demonstrating the existence of a methodological continuity from science to philosophy of 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 | 0.062 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.019 |
| Science and technology studies | 0.005 | 0.056 |
| Scholarly communication | 0.021 | 0.053 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.006 | 0.006 |
| 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".