Why Philosophy of Language is Unreliable for Understanding Unreliable Filmic Narration
Bibliographic record
Abstract
A typical device in film is to have a character narrating what is going on (sometimes by voice-over), but this narration is not always a reliable guide to the events. According to Maier, distortions may be caused by the narrator’s intent, naivety, use of drugs, and/or cognitive disorder/illness. What is common to these various causes, he argues, is the presence of a point of view, which appears in a movie as shots. While this perspective-based account of unreliability covers most cases, I unpack its methodological consequences and gesture at a possibility that Maier’s analysis overlooks. A narration, I suggest, can be unreliable simply because it is ill-timed with the events shown on screen. In such a case, the distortion is not due to any character’s point of view; rather, it comes from the film medium’s ability to divorce what is seen and what is heard. As a consequence of this mismatch, it is possible to have a reliable narrator but an unreliable narration. Since voice and context of utterance usually match in ordinary speech, I conclude that philosophy of language may be ill-suited to properly understand this particular phenomenon.
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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 | 0.001 | 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 | 0.002 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".