Social cohesion, social capital and social exclusion: A cross cultural comparison
Bibliographic record
Abstract
Interest in the concept of social cohesion has waxed and waned since Durkheim's foundation studies at the end of the 19th century, with the greatest interest being in times of fundamental economic, social and political change. The term is used in at least two different ways: firstly, in a policy context, to indicate the aims of, and rationale for, certain public policy actions; and secondly, as an analytical construct to explain social, political and sometimes economic changes. This article focuses on the first of these and traces the recent usage of social cohesion, spanning its take-up and influence within the Canadian policy environment, through to its usage (or otherwise) across liberal welfare regimes such as the UK, the US, Australia and New Zealand, and contrasting these experiences with its application in European institutions. The differential usage across these geopolitical settings is highlighted. Drawing upon Esping-Andersen's welfare state typology, and an explicit acknowledgement of national differences in relation to ethnic and cultural diversity, various explanations for these differences are discussed and their policy consequences explored.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.000 | 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".