A wolf in sheep's clothing? The interplay of perceived threat and social norms in hierarchy‐maintaining action tendencies towards disadvantaged groups
Bibliographic record
Abstract
Almost inherently, helping occurs between people with disparate resources. Consequently, the helping dynamic can reinforce power hierarchies, particularly regarding dependency-oriented helping (that preserves the power hierarchy) rather than autonomy-oriented helping (that may level power hierarchies). We posit that perceived social norms regarding helping disadvantaged groups affect the tendencies to help versus discriminate. Specifically, individuals who feel threatened by disadvantaged groups may conform to social norms by offering dependency-oriented help, thus preserving hierarchy while ostensibly adhering to societal expectations. Data from three correlational studies and one longitudinal study conducted in Germany (Studies 1a, 2a and 2b) and Israel (Study 1b) (combined N = 960) show that dependency-oriented help towards refugees is higher when participants perceive strong norms to help but feel threatened at the same time. This interaction was not visible for autonomy-oriented help. The finding is extended to a different intergroup setting (Study 3; N = 365) in which Jewish Israelis indicate higher intention to offer dependency-oriented help to Arab Israelis when there is a high threat and strong norms perceptions (in contrast to weak norms). The results have theoretical and practical implications for understanding factors that influence hierarchy-maintaining action tendencies and thereby intergroup inequality.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".