Individual, group, and temporal perspectives on the link between wealth and realistic threat
Bibliographic record
Abstract
In this 28-country study (N = 6112), we assessed how subjective perceptions and objective indicators of wealth were associated with majority group members’ perceptions of realistic threat related to immigration. Subjective wealth was assessed by individuals’ perceptions of their personal wealth (current/anticipated) and of their country´s wealth, whereas objective country-level wealth was assessed by GDP and HDI. Multilevel analyses showed that living in a country with a lower objective wealth and perceiving the country's relative wealth as low were associated with higher levels of perceived realistic threat. We also found that an anticipated decrease in personal wealth in the future was associated with higher threat perceptions only in low HDI countries. Our results suggest that perceived realistic threat is fostered by a perceived decline in the current wealth of the country, and country-level wealth may play a role in calibrating threat responses to anticipated personal wealth.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".