Bibliographic record
Abstract
Abstract Intellectual attention, such as perceptual attention, is a special mode of mental engagement with the world. When we attend intellectually, rather than making use of sensory information we make use of the kind of information that shows up in occurrent thought, memory, and the imagination. In this paper, I argue that reflecting on what it is like to comprehend memory demonstratives speaks in favor of the view that intellectual attention is required to understand memory demonstratives. Moreover, I argue that this is a line of thought endorsed by Gareth Evans in his Varieties of Reference. In so doing, I improve on interpretations of Evans that have been offered by Christopher Peacocke and Christoph Hoerl & Theresa McCormack. In so doing, I also improve on McDowell's criticism of Peacocke's interpretation of Evans. Like McDowell, I believe that Peacocke might overemphasize the role that “memory images” play in Evans’ account of comprehending memory demonstratives. But unlike McDowell, I provide a positive characterization of how Evans described the phenomenology of comprehending memory demonstratives.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".