Social cohesion in Europe: How do the different dimensions of inequality affect social cohesion?
Bibliographic record
Abstract
This article examines the relationships between social cohesion and social inequalities in Europe by considering three interrelated issues. The first regards the definition and measurement of the concept of social cohesion. The second issue concerns the identification of the aspects of social inequality that affect the overall level of social cohesion. More precisely, I investigate two main hypotheses: the first argues the existence of a direct negative association between economic inequality and social cohesion. The second states that this relation is influenced by other aspects of social inequality such as the individuals’ position in the stratification system and the educational level. The third issue introduces the comparative analysis that has been based on the welfare regime approach. More specifically, I hypothesize that welfare state is relevant because it influences both the relationship between social position and economic inequality, and the relationship between social cohesion and economic inequality. Data from the first round of the 2002 European Social Survey have been analysed applying structural equation models in order to measure social cohesion and to estimate the effects, both direct and indirect, exerted by the different dimensions of inequalities on social cohesion. Moreover, through a multi-group analysis, I investigate the effects resulting from the different welfare regimes. It emerges that social position and welfare state are not able to fully mediate the effect played by economic condition.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".