Audience Perception of Characters in Pedro Almodóvar’s Film La flor de mi secreto
Bibliographic record
Abstract
The study upon which this paper is based aims to contribute to the existing gap in audiovisual translation, that of audience perception, by exploring the relationship between subtitles, soundtrack, and audience perception. Two hundred undergraduate students took part in this investigation and were divided into three groups according to their nationalities: Spanish (Spain), British and North American (USA and Canada). The selected audiovisual material for the study was a six-and-a-half-minute clip from Pedro Almodóvar’s 1995 film La flor de mi secreto. Participants were shown the selected clip, subtitled in English, three times: twice with English subtitles only and the third time with the same subtitles plus the original soundtrack in Spanish. Participants were asked to complete a questionnaire in which they selected the most appropriate word/s in their native language which described the characters portrayed in the clip. The purpose of this exercise was to measure the effect of the subtitles on the participant’s word choice, firstly in isolation and then with the soundtrack. Results revealed differences in word choice for the description of the characters across nationalities
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".