Varieties of Supervenience. Technical Report 2001-03
Bibliographic record
Abstract
Supervenience is a fundamental concept for non-reductive physicalist theories of the \nmind (theories which hold that the physical level is the fundamental level of \nunderstanding, but which also hold that the mind cannot be reduced to the brain or any \nother physical level). Most computational theories of the mind belong to this category. In \nthis paper I will outline two different kinds of supervenience relationships and the \nproblems they face in explaining the mind-body problem. \nIn addition, I will argue that the more plausible version of supervenience is in conflict \nwith multiple realizability (the thesis that the mind can be instantiated by things other \nthan the brain), which is the philosophical basis of all Artificial Intelligence (AI) efforts. I \nthen suggest a possible way of saving both the supervenience relationship and multiple \nrealizability, taking inspiration from the Indian philosophical concept of autoreflexivity \nof awareness (svasamvedana).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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; both teacher heads agree on what is shown here.
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".