Narrative Permeability: Crossing the Dissociative Barrier in and out of Films
Bibliographic record
Abstract
Taking a cue from Arthur Frank's model of reading, this essay proposes a "thinking with" mode of engagement with narrative film, with the implicit aim of developing a pragmatic application for film viewing in narrative medicine. Few approaches to film take the feelings that attend or that are provoked by film seriously, despite the fact that emotions elicited while watching film feel very real to us. These are emotions with depth, emotions we have felt before, and are inexorably attached to specifics within the narratives of our own lives. This essay explores how the inherent characteristics of narrative film can provide fresh and sometimes surprising access to viewers' affective lives as dissociative barriers are temporarily relinquished. Viewers' attention to inner processing of their viewing experience may be usefully employed to gain greater access to processes of valuation and what Cathy Caruth terms "unclaimed experience," if we take time and attend, and if we develop techniques and skills for doing so.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.021 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".