Bibliographic record
Abstract
THE RELATIONSHIP BETWEEN INTENTIONALITY AND PHENOMENOLOGY Traditionally, postbehaviorist philosophy of mind and cognitive science has proceeded on the assumption that intentionality and phenomenology can most profitably be treated independently or separately from one another. The intentional and the phenomenal have often been viewed as the two fundamental categories of the mental within the philosophy of mind, with more specific types of mental states falling under one or the other of them. Belief and thought are paradigms of the intentional, and pain and bodily sensations paradigms of the phenomenal. Even when this divide between the intentional and the phenomenal has not been treated as a mutually exclusive categorization scheme for thinking about the constituents of the mind, intentional and phenomenal aspects of particular types of mental states remain distinguished and treated in separation from one another. Terence Horgan and John Tienson have called this general position separatism . These forms of separatism about the intentional and the phenomenal likely have no single cause. For some, separatism makes sense because intentionality is thought to be significantly more tractable than phenomenology. “Look,” one might say, “figuring out how the mind works is really hard. Consciousness is a complete mystery. But we at least have some inkling about intentionality and mental representation. So let's work that out first.” Hence, the explosion of work on causal, informational, and teleological theories of content, and the attention that the individualism-externalism debate has generated over the past twenty-five years.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.035 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".