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 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 | 0.016 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.058 |
| Scholarly communication | 0.010 | 0.031 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".