How Cohesive are Canadian CMAs? A Measure of Social Cohesion Using the National Survey of Giving, Volunteering, and Participating
Bibliographic record
Abstract
Social cohesion is a concept difficult to define and to measure. As there can be many definitions, so there can be many measurements. The main problem, either in defining or measuring the concept, is its multi-level and multi-dimensional nature. At one extreme, country is the most commonly used level to view social cohesion but measurement at this level is of little use for interventions. At the other extreme, community is the most useful level but it is a social construct for which data are difficult to obtain, given the administrative boundaries used in social surveys. As an initial attempt to measure social cohesion at a sub-country level, this study focuses on census metropolitan areas for which data on several dimensions of social cohesion are available. We use the information gathered by the National Survey on Giving, Volunteering and Participating (NSGVP) on three domains of social cohesion: political, economic, and social. Statistical techniques including factor analysis and standardization are applied to the data to generate an overall index of social cohesion for each CMA.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".