Do Borders Matter for Social Capital? Economic Growth and Civic Culture in U.S. States and Canadian Provinces
Bibliographic record
Abstract
The paper first assesses regional and ethnic group differences in social trust and memberships in both Canada and the United States. The ethnic categories people choose to describe themselves are as important as regional differences, but much less important than education, in explaining differences in trust. Respondents who qualify their nationality by any of seven adjectives, a feature more prevalent in the United States than in Canada, (black, white, Hispanic and Asian in the United States; French, English and Ethnic in Canada) have lower levels of trust than those who consider themselves Canadians or Americans either first or only. The dispersion of incomes across states or provinces has been dropping in both countries, but faster in Canada than in the United States. The 1980s increase in regional income disparity in the United States has no parallel in Canada. In neither country is there evidence that per capita economic growth is faster in regions marked by high levels of trust. However, U.S. migrants tend to move to states with higher perceived levels of trust, thus contributing to higher total growth in those states. The economic responsiveness of migration appears to be even stronger in Canada than in the United States, despite the much more extensive systems of fiscal equalization and social safety nets in Canada.
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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 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".