Bibliographic record
Abstract
Abstract: How does screen acting contribute to moral understanding? The most influential narratological theories of characters have been predominantly formalist in nature, focusing almost exclusively on attributed personality traits, behavior, dialogue, and/or visual appearance as the primary means of determining screened beings’ expressivity. Consequently, such theories fail to account for the aesthetics of the embodied performer, relegating acting to the subsidiary function of dramatic enaction rather than a necessary component of narration. A more complete account of film or television narration must consider the poetics of performance – the means by which actors’ expressive choices enable viewer comprehension and moral engagement. Indeed, concentrating primarily on character appearance, action, behavior, and dialogue gives us an incomplete picture of a work’s moral significance. Attending to such manifest content of character representation overlooks both its enacted form and certain aspects of its latent content. Therefore it is necessary to codify some of the means by which screen acting’s material elements can lead to moral appraisal. Drawing on certain key tenets of embodied cognition, we can schematize a conceptual vocabulary that enables us to attend to an actor’s expressive body and apprehend how this physicality draws us toward (or away from) the characters they represent. Of particular interest are the moral facets of actors’ appearances, expressions, gestures and postures, movements, and voices. These dimensions fundamentally inform how we might describe an ethically laden experience, evaluate characters as moral agents, and develop an embodied responsiveness to a work’s moral solicitations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".