Bibliographic record
Abstract
BRAIN THEORY: ESSAYS IN CRITICAL NEUROPHILOSOPHY Contents 0. Charles Wolfe (Ghent) Introduction Part I 1. Jean-Claude Dupont (Amiens) Memory traces between brain theory and philosophy 2. Stephen Gaukroger (Sydney) Pain and the Nature of Psychological Attributes 3. Jackie Sullivan (Western Ontario) Is the next frontier in neuroscience a ‘decade of the mind’? 4. Denis Forest (Paris-Nanterre) Neuroconstructivism: a developmental turn in cognitive neuroscience? Part II. 5. John Symons (Kansas) and Paco Calvo (Murcia) Computing with Bodies: Morphology, Function, and Computational Theory 6. Kellie Williamson and John Sutton (Macquarie) Embodied Collaboration in Small Groups 7. John Bickle (Mississippi State) Little-e eliminativism in mainstream cellular and molecular neuroscience: Tensions for neuro-normativity 8. William Hirstein and Katrina Sifferd (Elmhurst) Ethics and the Brains of Psychopaths: The Significance of Psychopaths for Ethical and Legal Reasoning 9. Sarah K. Robins (Kansas) Memory Traces, Memory Errors, and the Possibility of Neural Lie Detection Part III. 10. Sigrid Schmitz (Vienna) Feminist approaches to neurocultures 11. Luc Faucher (UQAM) Non-Reductive Integration in Social Cognitive Neuroscience: Multiple Systems Model and Situated Concepts 12. Nicolas Bullot (Macquarie) History, Causal Information, and the Neuroscience of Art: Toward a Psycho-Historical Theory 13. Warren Neidich (Berlin) The Architectonics of the Mind’s Eye in the Age of Cognitive Capitalism
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".