Bibliographic record
Abstract
Abstract Honor is complex, deeply relational, and important in many cultures and social groups. A definition of honor as multifaceted and consisting of a set of interrelated honor codes, i.e., the honor-as-multifaceted approach to honor, is presented and discussed by Rodriguez Mosquera. This definition provides researchers the conceptual boundaries of honor as a construct as well as methodological guidelines on how to operationalize honor in empirical research. Furthermore, the honor-as-multifaceted approach provides researchers with a definition of cultures of honor as those in which honor codes become culturally shared psychological concerns that individuals evaluate as important to their self-esteem and self-concept, thereby influencing their cognitions, motivations, emotions, and behaviors. The Honor Scale measures honor codes in line with this definition. A review of existing empirical research on honor in a wide variety of cultures and social groups is also presented and discussed. Some of the work reviewed is cross-cultural in nature, whereas other work focuses on how honor operates in particular cultures or social groups (e.g., British Muslims; Moroccan Dutch and Turkish Dutch youth; Southern Italian criminal organizations; the Canadian Army). The reviewed research provides empirical support for the honor-as-multifaceted approach and demonstrates the centrality of honor codes in a variety of psychological and social processes, including personality, the negotiation of gendered roles within the family, attitudes toward in-group members, emotions in response to threats to collective honor, intergroup conflict, the negotiation of power in intergroup relations, in-group identification processes, and prosocial motivations. Thus, the reviewed research shows that honor codes play an important role in processes at the different levels of analysis typically studied in the social sciences—individual, interpersonal, group, cultural—thereby making honor an important topic of inquiry for psychologists and other social scientists. Avenues for future research are also 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".