Phenomenal Intentionality and Derived Intentionality A Commentary on David Pitt's <i>The Quality of Thought</i>
Bibliographic record
Abstract
David Pitt's The Quality of Thought defends a hardcore version of phenomenal intentionalism, the view that at least the most basic kind of intentionality is nothing over and above phenomenal consciousness. The book is focused on the case of thought, advancing the view, roughly, that thoughts' contents are identical to their phenomenal characters. But the view is meant to apply more broadly to all intentional states: for Pitt, the content of any intentional state is identical to its phenomenal character. One of the most pressing challenges facing phenomenal intentionalism is that of accounting for allegedly intentional unconscious mental states. In response to this challenge, many phenomenal intentionalists develop a two-tiered picture of intentionality, maintaining that the most basic kind of intentionality, original intentionality, is nothing over and above phenomenal consciousness but that mental states can have another, less basic, kind of intentionality, derived intentionality, which is derived in some way from actual or possible originally intentional states. Pitt rejects this derivativist picture and maintains a hard-line position on which non-phenomenal states cannot have any kind of intentionality. In this symposium contribution, I consider Pitt's arguments against derivativist pictures of intentionality, arguing that the derivativist has a ready response. I suggest that even if derived intentionality belongs to a different natural kind than original intentionality, it plays an important role in the mind.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.023 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.013 | 0.035 |
| 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".