Social Capital, Social Cohesion, and Population Outcomes in Canada’s First Nations Communities
Bibliographic record
Abstract
Communities are social constructions built through the interaction of human actors with each other and with their environment.As such, the community is both a physical entity and a relationship.Organizations, institutions, structures of custom and patterns of everyday life are products of our interrelationships in our communities.These interrelationships can produce cohesion and solidarity or discord and disunity.Whether we believe it is the sameness of life that produces cohesion (as in Durkheim's mechanical society), or the class consciousness that defines world views, solidarity effects people's well-being and their social and economic achievements.The sociology of this century has demonstrated a fascination with cohesion and the lack of it in human community.From the anomic post-structural angst of anti-positivism to the Colemanesk social capitalist constructions of trust, the problems we, as sociologists focus on are similar.What are the features of our communities that explain the human condition?Can we come to understand them and even predict their effect?This paper is the first of a series that contribute to this quest for understanding.We consider that this paper provides the foundation for our research agenda.In this research agenda we are concerned in general with determining what factors in the make-up and functioning of communities contribute to differential population outcomes.This project is anchored in the sociology of policy
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".