Bibliographic record
Abstract
Donald Price and James Barrell, two eminent pain researchers, argue that every time an experiment demonstrates that a biological variable is causally relevant to a psychological outcome, we are entitled to further rule out a possible mind–brain identity or supervenience relationship. As experimental evidence for two-way causal links between biological and psychological variables accumulates, more and more identity and supervenience relationships are ruled out, suggesting that psychophysicalism is empirically better supported than physicalism. I raise an objection to this line of argumentation by pointing out that, in the studies in question, causation is established within an operationalized framework; that is, irrespective of whether one knows what exactly is being manipulated and measured and how the intervention and measurement techniques work. By itself, evidence for causal relevance doesn't demonstrate that to each manipulated variable corresponds an ontologically distinct cause. This opens the possibility that some variables share common referents, as postulated by physicalist accounts. Moreover, even if it is not clear how to test for identity or supervenience relationships, it is still possible to test for causal mediation, which can generate empirical evidence discriminating between reductive physicalism and non-reductive alternatives.
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 | 0.037 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.026 |
| Scholarly communication | 0.005 | 0.020 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.018 | 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".