Bibliographic record
Abstract
In these comments, I focus on three questions raised by Silva’s theory of awareness and propose an amendment to his original account. Silva’s (2023) book aims to put a new category on the table in epistemology: awareness. To be aware of a fact is to have a representation of it that is true and brought about in the right way. On some versions of awareness, ‘brought about in the right way’ means ‘brought about in a way that makes the representation non-accidentally true’. At the end of the book, Silva also puts forward a version of awareness which does not make explicit reference to luck. On this view, ‘brought about in the right way’ means ‘brought about by cognitive abilities to form true representations’. For instance, imagine I glance at a piece of paper and see that the letter is from my sister. I do this so quickly and subliminally that I can’t be said to believe the letter is from my sister. Moreover, I might hold misleading evidence that suggests the letter cannot be from my sister: she told me that she hates writing letters now and only sends emails. But in any case, I register that the letter is from my sister without believing it through my cognitive abilities, in this case attention and perception, which function to produce and maintain true representations. Thus I am aware that the letter is from my sister.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".