Willingness to help: how the portrayal and perception of a wrongfully convicted individual affects people???s willingness to help exonerees
Bibliographic record
Abstract
Currently, in Canada there is no legal requirement to compensate exonerees (Roach, 2012), and despite research suggesting Canadians would be supportive of this government assistance for exonerees (Angus Reid, 1995; Clow, Blandisi, et al., 2012), Canada rarely compensates or provides them with reintegration services (Schuller et al., 2021). Two studies were conducted to examine how emotions and empathic concern might impact personal willingness to help exonerees. In both studies, participants watched a video of an exoneree discussing an angry or sad aspect of his wrongful conviction and then asked about helping exonerees (assessed with both self-report and behavioural measures). Emotions were manipulated and/or measured a few different ways in each study. Participants??? sadness about the exoneree???s story and empathic concern increased self-reported helping, yet video condition had little impact. Behavioural helping was less consistent across studies. The findings are discussed in the context of education and increasing support for exonerees.
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.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".