Database of Emotional Videos from Ottawa (DEVO)
Bibliographic record
Abstract
We present a collection of emotional video clips that can be used in ways similar to static images (e.g., the International Affective Picture System, IAPS; Lang, Bradley, & Cuthbert, 2008). The Database of Emotional Videos from Ottawa (DEVO) includes 291 brief video clips (mean duration = 5.42 s; SD = 2.89 s; range = 3–15 s) extracted from obscure sources to reduce their familiarity and to avoid influencing participants’ emotional responses. In Study 1, ratings of valence and arousal (measured with the Self Assessment Manikins from IAPS) and impact (Croucher, Calder, Ramponi, Barnard, & Murphy, 2011) were collected from 154 participants (82 women; mean age = 19.88 years; SD = 2.83 years), in a between-subjects design to avoid potential halo effects across the three ratings (Saal, Downey, & Lahey, 1980). Ratings collected online in a new set of 124 students with a within-subjects design (Study 2) were significantly correlated with the original sample’s. The clips were unfamiliar, having been seen previously by fewer than 2% of participants on average. The ratings consistently revealed the expected U-shaped relationships between valence and arousal/impact, and a strong positive correlation between arousal and impact. Hierarchical cluster analysis of the Study 1 ratings suggested seven groups of clips varying in valence, arousal, and impact, although the Study 2 ratings suggested five groups of clips. These clips should prove useful for a wide range of research on emotion and behaviour.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.013 |
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".