Modernization, formal social control, and anomie: A 45-society multilevel analysis
Bibliographic record
Abstract
This article investigates how economic modernization affects normative regulation by spurring formal social control in the political, economic, and private spheres as well as anomie. Multilevel negative binomial regression modeling, using World Values Survey and country-level data from 2005, predicts individual-level anomie using country-level formal-social-control indicators as well as individual-level controls. Such control variables include education, survey interest, gender, age, income, collectivism, nihilism, fatalism, and the diversity of information consumption. This work argues for and implements a ‘don’t know anomie’ (DKA) index, the sum of ‘don’t know’ responses in relation to 15 attitudinal questions, as a more direct measure of individual-level anomie. Findings indicate that, when controlling for all factors, a country’s level of formal social control in the political sphere, measured as low levels of perceived government corruption, reduces anomie. In addition, country-level formal social control in the private sphere, operationalized as a society where individuals are not primarily striving to meet their parents’ expectations, enhances anomie.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".