Bibliographic record
Abstract
Bas Van Fraassen is a nifty philosopher of science. He received his PhD in Pittsburgh in 1966, under the guidance of Adolf Grünbaum, he taught at Yale University, the university of Toronto, the University of Southern California, he has been McCosh Professor of Philosophy in Princeton, and eventually joined the department of philosophy at San Francisco State University, where he has the title of Distinguished Professor of Philosophy. He first gained attention with his book An Introduction to the Philosophy of Time and Space where he tried to develop a formal theory of space and time based on the notion of causality. The book had an enormous legacy, with experts of the likes of John Earman and David Malament joining the debate. However, he achieved V.I.P. status with his classic The Scientific Image, where he defends a combination of empiricism and antirealism towards unobservable entities based on a re-definition of what the scientific enterprise is. His last achievement is the tome Scientific Representation: Paradoxes of Perspectives, where he combines his scientific empiricism with the view that theories are best thought as models or structures, rather than sets of sentences. In this interview, we talk about his philosophical influences and the birth of The Scientific Image during a journey through North-Africa, Turkey and Eastern Europe, we talk about saving the phenomena and suspending judgement over the existence of unobservable entities, living in world full of mysteries and leaving unanswerable questions unanswered, rationality and irrationality, living in a simulation, the historical interplay between theorizing and experimenting, the meaning of particle detectors for an empiricist, the unity of science and physicalism, the condemnation of Galilei by the Church, and the distinction between Appearance and Reality…
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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.009 | 0.027 |
| Insufficient payload (model declined to judge) | 0.061 | 0.018 |
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".