Institutional imbalance, integration into Non-economic institutions, and a marketized mentality in Europe: A multilevel, partial elaboration of Institutional Anomie Theory
Bibliographic record
Abstract
This research builds upon prior efforts to transport insights from a macro-sociological theory of crime – Institutional Anomie Theory (IAT) – to enhance understanding of an important individual-level phenomenon in advanced capitalist societies – a ‘marketized mentality’. Such a mentality entails a strong commitment to the utilitarian, instrumental values of the market at the expense of more altruistic, expressive values. Drawing upon IAT, we hypothesize that at the individual level, integration into selected non-economic institutions will tend to inhibit the adoption of a marketized mentality, but the strength of this inhibiting effect will vary depending on the degree to which economic institutions and non-economic institutions in society are ‘balanced’ or ‘unbalanced’. Our theoretical arguments imply that observed levels of acceptance of a marketized mentality will vary significantly across societies and that this variation will be related to the degree of imbalance in the institutional order: We expect to observe a cross-level interaction such that the inhibiting effect of integration into non-economic institutions is attenuated as institutional imbalance increases. These hypotheses are assessed in multilevel analyses with data from 25 European countries using the European Social Survey. The results are generally in accordance with theoretical expectations, revealing how an institutional imbalance helps shape people’s value orientations by promoting marketized mentalities and by weakening the socialization effects of non-economic institutions.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".