Perspective taking, blame, and prejudice: Does blame mediate the relationship between perspective taking and prejudice?
Bibliographic record
Abstract
Perspective taking (PT) is the ability to understand the world from another person’s point of view. It plays an important role in fostering positive intergroup interactions and has been linked to reduced prejudice as well as shifts in attributions of blame (Galinsky & Ku, 2004; Wang et al., 2014). Specifically, it encourages people to consider situational factors contributing to a stranger’s actions, rather than blaming the individual themselves (Hooper et al., 2015; Hu et al., 2016). Together, this previous work suggests that greater PT is associated with lower levels of prejudice and reduced feelings of blame. However, some studies indicate that PT’s link to reduced prejudice is weaker when directed toward a target perceived as responsible for their circumstances (Adikaram & Kailasapathy, 2024; Batson et al., 1997). The current study investigates the relationship between trait-level PT and prejudice toward people living in poverty and examines how it is influenced by feelings of blame toward that same group. Specifically, we will conduct a mediation analysis to better understand whether PT’s effect on prejudice is influenced by feelings of blame when the target is perceived as blameworthy, as is often the case with people living in poverty, who are frequently viewed by society as responsible for their circumstances (Alcañiz-Colomer et al., 2024; Godfrey & Wolf, 2015). Our sample will consist of undergraduate students recruited from the University of British Columbia in Vancouver, Canada. Perspective taking, blame, and prejudice toward people living in poverty will be assessed using established and validated self-report measures administered through Qualtrics surveys (Babij et al., 2023; Clutterbuck et al., 2021; Crandall, 1994).
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| 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".