Can resource depletion explain the differing effects of perspective-taking on racial bias?
Bibliographic record
Abstract
Perspective-taking has become a popular technique for mitigating racially biased behavior, yet some studies, like Vorauer, Martens, and Sasaki (2009) have found it can also backfire. One possible explanation for the results seen in Vorauer et al. (2009) is differing cognitive demands when participants are primed with a perspective-taking mindset from a previous task (indirect perspective-taking; IPT), or if they’re actively manipulated to do so in the interaction being measured (direct perspective-taking; DPT). This study recruited White American and Canadian adults using Amazon’s Mechanical Turk (MTurk) for a study ostensibly involving a video chat with another participant. Participants were randomly assigned a depletion condition (depletion or non-depletion) and a mindset manipulation (objective, DPT, or IPT). Intimacy-building behavior was measured in personal questionnaires participants filled out to ostensibly exchange with their video chat partner. It was hypothesized that depletion would explain differences in IPT and DPT interventions, however intimacy-building behavior did not vary significantly, and was uniformly low.
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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".