Social Institutions and Sanctioned Behaviors: A Cross-National Study
Bibliographic record
Abstract
This research considers important social institutions (e.g., the economic system, the level of industrialization, the level of social inequality, and the degree of religiosity) as determinants of individuals’ justifications to commit socially sanctioned behaviors. Using factor analyses on data from 32,734 individuals located in 27 nations, we find that regardless of country, all individuals group 23 socially sanctioned behaviors uniformly in three categories, which we term controversial behaviors (e.g., abortion), peccadilloes (e.g., keeping money found), and illegal behaviors (e.g., political assassinations). We used Hierarchical Linear Modeling (HLM) to test the country-level effects of the social institutions on individuals’ ability to justify these three types of sanctioned behaviors. The results confirm that the social institutions influence individuals’ justifications of sanctioned behaviors, above and beyond important individual-level control variables included in the HLM analyses. The economic system (degree of socialism) and the level of industrialization show positive effects on all three types of sanctioned behaviors. Social inequality has a positive effect on illegal behaviors and peccadilloes, but a negative effect on controversial behaviors. Religiosity affects illegal behaviors positively and controversial behaviors negatively with no significant influence on peccadilloes.
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 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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".