Bibliographic record
Abstract
Notes on Contributors 1. Introduction Steven French and Juha Saatisi (University of Leeds, UK) Part I: Philosophy of Science in Context 2. Philosophy of Science and Epistemology Alexander Bird (University of Bristol, UK), 3. Philosophy of Science and Metaphysics Craig Callendar (University of California, San Diego, USA) 4. Philosophy of Science and the History of Science Don Howard (University of Notre Dame, USA) Part II: Current Research and Issues A. General Issues in the Philosophy of Science 5. Scientific Realism with a Human Face Stathis Psillos (University of Athens, Greece) 6. Causation and the Sciences Ned Hall (Harvard University, USA) 7. Scientific Models and Representation Gabriele Contessa (Carleton University, USA) 8. Reducation, Multiple Realizability and Levels of Reality Sven Walter (University of Osnabrueck, Austria) and Markus Eronen 9. Explanation Henk W.de Regt (University of Amsterdam, Netherlands) 10. Scientific Evidence Malcolm Forster (University of Wisconsin, USA) 11. Bayesian Confirmation Theory James Hawthorne (University of Oklahoma, USA) B. Philosophy of Particular Sciences 12. Philosophy of Physics Nick Huggett (University of Illinois at Chicago, USA) 13. Philosophy of Biology Ingo Brigandt (University of Alberta, Canada) 14. Towards a Mechanistic Philosophy of Neuroscience Carl F. Craver (Washington University, USA) and David M. Kaplan 15. Philosophy of Chemistry Robin Findlay Hendry (Durham University, UK) 16.Philosophy of Economics, Jaakko Kuorikoski and Caterina Marchionni (Finnish Centre of Excellence in Philosophy of Social Science, University of Helsinki, Finland) 17. Philosophy of Mathematics Chris Pincock (Purdue University, USA) Part III: Past and Future 18. Travelling in New Directions 19. A Brief Chronology of the Philosophy of Science Peter Vicers Part IV: Resources 20. Annotated Bibliography 21. Research Resources 221. A to Z of Key Terms and Concepts Index
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Science and technology studies Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.269 | 0.168 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".