Social Cohesion: Updating the State of the Research By
Bibliographic record
Abstract
When social policy is regarded solely from the perspective of economics, there is no lack of “big picture concepts ” to frame policy discourse. Gross domestic product serves as the measure to gauge economic progress, while competitiveness and globalization are buzzwords that capture some of the biggest forces affecting the trends in economic activity. It was only in the 1990s that social policy analysts found an expression for the big picture framing of their discourse – social cohesion. This concept, or quasi-concept, emerged in the first half of the 1990s in Europe and in Canada. It filled a big gap in the analytic language, serving as the term which captures the “macro ” picture for social policy discussion. As Jane Jenson pointed out in her 1998 paper, Social Cohesion: The State of the Canadian Research, social cohesion meant different things to different people, even as it inspired a burst of intellectual activity in international organizations, national bureaucracies, universities and think tanks, indeed in policy communities in general. That paper unpacked the different meanings and described the state of thinking as it existed at that time. The research and the debate have flourished since 1998, and this paper provides a structured analysis of the direction the literature has taken over the past four years. Caroline Beauvais and
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 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 | 0.123 | 0.157 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.024 | 0.019 |
| Science and technology studies | 0.010 | 0.069 |
| Scholarly communication | 0.036 | 0.066 |
| Open science | 0.006 | 0.017 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".