Lifelong Learning and Social Cohesion
Bibliographic record
Abstract
An extensive literature has evolved, particularly in the United States, around the notion of social cohesion. This literature marks a shift away from sociological ideas of the past. It reflects the profound economic and social changes that societies have undergone in the last 30 years under the impact of successive crises, the development of market relations in most spheres of social life and the effects of globalisation. As at other times in the past, these changes call into question what it means ‘to live in society’. The notion of social cohesion has been the subject of considerable debate (for example Chan et al., 2006; Green et al., 2006; Dubet et al., 2010). The arguments have been all the more intense since various countries have put the spotlight on social cohesion as the foundation and/or goal of public policies. Thus the European Union (EU), for example, has offered member states a set of targets and indicators linked to social cohesion as part of the ‘European social model’. Similarly, in Canada (Patrimoine Canadien, 2004; Jeannotte, 2000) several government reports have examined this issue. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".