Bibliographic record
Abstract
I discuss the apparent discrepancy between the qualitative diversity of consciousness and the relative qualitative homogeneity of the brain’s basic constituents, a discrepancy that has been raised as a problem for identity theorists by Maxwell and Lockwood (as one element of the ‘grain problem’), and more recently as a problem for panpsychists (under the heading of ‘the palette problem’). The challenge posed to panpsychists by this discrepancy is to make sense of how a relatively small ‘palette’ of basic qualities could give rise to the bewildering diversity of qualities we, and presumably other creatures, experience. I argue that panpsychists can meet this challenge, though it requires taking contentious stands on certain phenomenological questions, in particular on whether any familiar qualities are actual examples of ‘phenomenal blending’, and whether any other familiar qualities have a positive ‘phenomenologically simple character’. Moreover, it requires accepting an eventual theory most elements of which are in a certain explicable sense unimaginable, though not for that reason inconceivable. Nevertheless, I conclude that there are no conclusive reasons to reject such a theory, and so philosophers whose prior commitments motivate them to adopt it can do so without major theoretical cost.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.047 |
| Scholarly communication | 0.006 | 0.023 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".