Perceptions of the Targets and Sources of COVID-19 Threat are Structured by Group Memberships and Responses are Influenced by Identification with Humankind
Bibliographic record
Abstract
The purpose of this study was to investigate which social groups are perceived as a threat target and which are perceived as a threat source during the COVID-19 outbreak. In a German sample (N = 1454) we examined perceptions of social groups ranging from those that are psychologically close and smaller (family, friends, neighbors) to those that are more distal and larger (people living in Germany, humankind). We hypothesized that psychologically closer groups would be perceived as less affected by COVID-19 as well as less threatening than more psychologically distal groups. Based on social identity theorizing, we also hypothesized that stronger identification with humankind would change these patterns. Furthermore, we explored how these threat perceptions relate to adherence to COVID-19 health guidelines. In line with our hypotheses, latent random-slope modelling revealed that psychologically distal and larger groups were perceived as more affected by COVID-19 and as more threatening than psychologically closer and smaller groups. Including identification with humankind as a predictor into the threat target model resulted in a steeper increase in threat target perception patterns, whereas identification with humankind did not predict differences in threat source perceptions. Additionally, an increase in threat source perceptions across social groups was associated with more adherence to health guidelines, whereas an increase in threat target perceptions was not. We fully replicated these findings in a subgroup from the original sample (N = 989) four weeks later. We argue that societal recovery from this and other crises will be supported by an inclusive approach informed by a sense of our common identity as human beings.
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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 | 0.001 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".