Bibliographic record
Abstract
In his paper 'Are we ever aware of concepts? A critical question for the Global Neuronal Workspace, Integrated Information, and Attended Intermediate-Level Representation theories of consciousness' (2015, this journal), Kemmerer defends a conservative account of consciousness, according to which concepts and thoughts do not characterize the contents of consciousness, and then uses that account to argue against both the Global Neuronal Workspace theory of consciousness and Integrated Information Theory of Consciousness, and as a point in favour of Prinz's Attended Intermediate-level Representations theory. We argue that there are a number of respects in which the contrast between conservative and liberal conceptions of the admissible contents of consciousness is more complex than Kemmerer's discussion suggests. We then consider Kemmerer's case for conservatism, arguing that it lumbers liberals with commitments that they need not - and in our view should not - endorse. We also argue that Kemmerer's attempt to use his case for conservatism against the Global Neuronal Workspace and Integrated Information theories of consciousness on the one hand, and as a point in favour of Prinz's Attended Intermediate Representations theory on the other hand, is problematic. Finally, we consider Kemmerer's overall strategy of using an account of the admissible contents of consciousness to evaluate theories of consciousness, and suggest that here too there are complications that Kemmerer's discussion overlooks.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.027 |
| Scholarly communication | 0.007 | 0.015 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".