Christian face representations are rated more positively than Muslim face representations
Bibliographic record
Abstract
People store mental representations of faces for various social categories (Dotsch et al., 2011). These mental representations can reflect biases regarding social groups. This two-part study first used the reverse correlation paradigm (Mangini & Biederman, 2004) to create images of the mental representations that Christian and Muslim Canadians have of Christian and Muslim faces. 20 Christian and 20 Muslim participants were presented with a two-image forced choice task – each image was the average of 60 neutral faces, overlayed with a randomly generated Gaussian noise pattern – and were asked in some trials to select the face that look Christian, or in other trials, Muslim. There were a total of four blocks, two for each religion, across two male and female blocks. The selected images were then averaged to create classification images (CIs) which are proxy images of mental representations of Christian and Muslim faces (Brinkman et al., 2017; Dotsch & Todorov, 2012). In the second part of the study, a new sample of 252 naive participants rated the CIs on several valenced characteristics (e.g., happiness, trustworthiness, warmth) and several demographic characteristics (e.g., gender and ethnicity) to probe the original participants’ attitudes towards Christians and Muslims. Regardless of the religious identity of the participants who generated the CI, Christian CIs were consistently rated more positively than Muslim CIs (ꭓ2’s > 55, p’s < 0.001). There was no such pattern for the demographic characteristics (ꭓ2’s > 7, p’s > 0.05). These results favour the idea that both Christians and Muslims have an implicit bias in favour of Christianity, the dominant religion in Canada, over ingroup bias.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".