Denver pain authenticity stimulus set (D-PASS)
Bibliographic record
Abstract
We introduce the Denver Pain Authenticity Stimulus Set (D-PASS), a free resource containing 315 videos of 105 unique individuals expressing authentic and posed pain. All expressers were recorded displaying one authentic (105; pain was elicited via a pressure algometer) and two posed (210) expressions of pain (one posed expression recorded before [posed-unrehearsed] and one recorded after [posed-rehearsed] the authentic pain expression). In addition to authentic and posed pain videos, the database includes an accompanying codebook including metrics assessed at the expresser and video levels (e.g., Facial Action Coding System metrics for each video controlling for neutral images of the expresser), expressers' pain threshold and pain tolerance values, averaged pain detection performance by naïve perceivers who viewed the videos (e.g., accuracy, response bias), neutral images of each expresser, and face characteristic rating data for neutral images of each expresser (e.g., attractiveness, trustworthiness). The stimuli and accompanying codebook can be accessed for academic research purposes from https://digitalcommons.du.edu/lsdl_dpass/1/ . The relatively large number of stimuli allow for consideration of expresser-level variability in analyses and enable more advanced statistical approaches (e.g., signal detection analyses). Furthermore, the large number of Black (n = 41) and White (n = 56) expressers permits investigations into the role of race in pain expression, perception, and authenticity detection. Finally, the accompanying codebook may provide pilot data for novel investigations in the intergroup or pain sciences.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.083 | 0.010 |
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".