Bibliographic record
Abstract
Notes on Contributors.Preface.Introduction: What is the Philosophy of Science?.Part I: Do Thought Experiments Transcend Empiricism?.1. Why Thought Experiments Transcend Empiricism: James Robert Brown (University of Toronto).2. Why Thought Experiments do not Transcend Empiricism: John Norton (University of Pittsburgh).Part II: Does Probability Capture the Logic of Scientific Confirmation or Justification?.3. Probability Captures the Logic of Scientific Confirmation: Patrick Maher (University of Illinois at Urbana-Champaign).4. Why Probability Does not Capture the Logic of Scientific Justification: Kevin Kelly (Carnegie Mellon University) and Clark Glymour (Carnegie Mellon University).Part III: Can a Theory's Predictive Success Warrant Belief in the Unobservable Entities it Postulates?.5. A Theory's Predictive Success Can Warrant Belief in the Unobservable Entities it Postulates: Jarrett Leplin (University of North Carolina, Greensboro).6. A Theory's Predictive Success Does not Warrant Belief in the Unobservable Entities it Postulates: Andre Kukla (University of Toronto) and Joel Walmsley (University of Toronto).Part IV: Are There Laws in the Social Sciences?.7. There are no Laws in the Social Sciences: John Roberts (University of North Carolina, Chapel Hill).8. There are Laws in the Social Sciences: Harold Kincaid (University of Alabama at Birmingham).Part V: Are Causes Physically Connected to their Effects?.9. Causes are Physically Connected to Their Effects: Why Preventers and Omissions are not Causes: Phil Dowe (University of Queensland, Australia).10. Causes Need Not be Physically Connected to their Effects: The Case for Negative Causation: Jonathan Schaffer (University of Massachusetts, Amherst).Part VI: Is There a Puzzle about the Low Entropy Past?.11. On the Origins of the Arrow of Time: Why There is Still a Puzzle About the Low Entropy Past: Huw Price (University of Edinburgh).12. There is No Puzzle About the Low Entropy Past: Craig Callender.Part VII: Do Genes Encode Information About Phenotypic Traits:.13. Genes Encode Information for Phenotypic Traits: Sahotra Sarkar (University of Texas at Austin).14. Genes Do not Encode Information for Phenotypic Traits: Peter Godfrey-Smith (Stanford University).Part VIII: Is the Mind a System of Modules Shaped by Natural Selection?.15. The Mind is a System of Modules Shaped by Natural Selection: Peter Carruthers (University of Maryland).16. The Mind is Not (Just) a System of Modules Shaped (Just) by Natural Selection: Fiona Cowie (California Institute of Technology) and James Woodward (California Institute of Technology).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 | Theoretical or conceptual | low |
| gpt | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | 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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.033 | 0.011 |
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".