UNINSTRUCTED PARTICIPANTS USE PAIN SCALES TO INDICATE SOCIAL DISTRESS
Bibliographic record
Abstract
Background:In common usage, the term u201cpainu201d is often used to denote emotional distress. In recent years, this broader use of the term has also permeated the scientific literature, notably to describe the distress of social exclusion (u201csocial painu201d) (Eisenberger, 2015). This raises the possibility that individuals might use pain scales to reflect their current emotional state, creating ambiguity around self-reported pain in clinical and experimental settings. To examine this possibility, we elicited social distress and then asked participants to rate pain both with and without explicit instructions on what should be considered pain. Methods:Forty participants played a computerised ball-tossing game in a three-player social exclusion paradigm (cyberball). Participants initially received the ball twice, but were excluded on all subsequent trials. Following the trials, participants were asked to rate their pain and unpleasantness. To determine whether explicit instruction would alter pain rating behaviour, half the participants were given the IASP pain definition prior to making their ratings.Results:Despite receiving no nociceptive stimulation, participants rated the cyberball experience as painful, indicating that u201cpainu201d was being used to indicate a negative emotional state. This tendency was significantly reduced for individuals given the IASP definition prior to the cyberball trial (M=3.65 for uninstructed individuals, M=1.95 for instructed; t=2.18, p<0.01). Conclusion:These results indicate that participants will use pain scales to rate their emotional state, even in the absence of any nociceptive stimulation. This behaviour was significantly reduced
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".