Denver pain authenticity stimulus set (D-PASS)
Why this work is in the frame
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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.
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 | 0.020 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 it