Eliciting emotion ratings for a set of film clips: A preliminary archive for research in emotion
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Film clips are commonly used to elicit subjectively experienced emotional states for many research purposes, but film clips currently available in databases are out of date, include a limited set of emotions, and/or pertain to only one conceptualization of emotion. This work reports validation data from two studies aimed to elicit basic and complex emotions (amusement, anger, anxiety, compassion, contentment, disgust, fear, happiness/joy, irritation, neutrality, pride, relief, sadness, surprise), equally distributed according to valence (positive, negative) and intensity (high, low). Participants rated film clips according to the degree of experienced emotion, and for valence and arousal. Our findings initiate an iterative archive of film clips shown here to discretely elicit 11 different emotions. Although further validation of these film clips is needed, ratings provided here should assist researchers in selecting potential film clips to meet the aims of their work.
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.
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 | 0.003 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it