Bibliographic record
Abstract
abstract It is common parlance among philosophers who inquire into the nature of consciousness to speak of there beingsomething it is likefor the subject of a mental state to be in it. The popularity of the ‘what‐it‐is‐like’ phrase stems, in part, from the assumption that it enables us to distinguish, in an intuitive and illuminating way, between conscious and unconscious mental states: conscious mental states, unlike unconscious mental states, are such that there is something it is like for their subjects to be in them. The ‘what‐it‐is‐like’ phrase, however, has not gone unopposed; some very clever philosophers have vigorously disputed it. Peter Hacker, for example, argues that the phrase should be abandoned because it is ungrammatical, and Paul Snowdon argues that it should be abandoned because the propositions expressed by its usage are either trivial or false. This paper mounts a case for the claim that neither of these conclusions is warranted. Against Hacker, it is argued that the arguments he produces for the ungrammaticality of the phrase are unpersuasive; and, against Snowdon, it is argued that he fails to consider a plausible and independently motivated interpretation of the phrase and that on this interpretation, the propositions expressed by its usage are nontrivially true.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.042 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".