Salience Matters: Filler groups on the ascent of human scale impact ratings for target groups
Bibliographic record
Abstract
Researchers using the ascent of human scale (AOH) to study dehumanization typically include filler groups in addition to the main comparator groups, to hide the true intent of the study. However, there is little work examining the impact of filler group choice on dehumanization ratings between groups of interest. Across two studies (including one pre-registered study) we manipulated the salience of a target out-group (i.e., the extent to which the group stood out) by embedding it within lists of other groups. By comparing AOH ratings across three conditions in which the target out-group was either high salience, medium salience, or low salience, we were able to determine the effects of target out-group salience on dehumanization. In study 1, we included participants' in-group (Canadian) in the list, and in study 2, we did not include participants in-group in the list. Results from study 1 showed that group salience had no impact on AOH ratings for the out-group when the participant in-group was included in the list. However, in study 2, when participant in-group was removed from the list, ratings for the out-group in the high salience condition were significantly lower than both the medium and low salience conditions. Implications for both theoretical and methodological issues in investigations using the AOH scale are discussed.
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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 | 0.010 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".