Bibliographic record
Abstract
A classic and fraught question in the philosophy of film is this: when you watch a film, do you experience yourself in the world of the film, observing the scenes? In this paper, we argue that this subject of film experience is sometimes a mere impersonal viewpoint, sometimes a first-personal but unindexed subject, and sometimes a particular, indexed subject such as the viewer herself or a character in the film. We first argue for subject pluralism: there is no single answer to the question of what kind of subjectivity, if any, is mandated across film sequences. Then, we defend unindexed subjectivity: at least sometimes, films mandate an experience that is first-personal but not tied to any particular person, not even to the viewer. Taken together, these two theses allow us to see film experience as more varied than previously appreciated and to bridge in a novel way the cognition of film with the exercise of other imaginative capacities, such as mindreading and episodic recollecting.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".