The distinct associations of ingroup attachment and glorification with responses to the coronavirus pandemic: Evidence from a multilevel investigation in 21 countries
Bibliographic record
Abstract
While public health crises such as the coronavirus pandemic transcend national borders, practical efforts to combat them are often instantiated at the national level. Thus, national group identities may play key roles in shaping compliance with and support for preventative measures (e.g., hygiene and lockdowns). Using data from 25,159 participants across representative samples from 21 nations, we investigated how different modalities of ingroup identification (attachment and glorification) are linked with reactions to the coronavirus pandemic (compliance and support for lockdown restrictions). We also examined the extent to which the associations of attachment and glorification with responses to the coronavirus pandemic are mediated through trust in information about the coronavirus pandemic from scientific and government sources. Multilevel models suggested that attachment, but not glorification, was associated with increased trust in science and compliance with federal COVID-19 guidelines. However, while both attachment and glorification were associated with trust in government and support for lockdown restrictions, glorification was more strongly associated with trust in government information than attachment. These results suggest that both attachment and glorification can be useful for promoting public health, although glorification's role, while potentially stronger, is restricted to pathways through trust in government information.
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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".