The place of social capital in understanding social and economic outcomes. Canadian Journal of Policy Research
Bibliographic record
Abstract
It is hardly possible to overrate the value...of placing human beings in contact with persons dissimilar to themselves, and with modes of thought and action unlike those with which they are familiar... Such communication has always been, and is peculiarly in the present age, one of the primary sources of progress. John Stuart Mill This paper provides a brief introduction to the recent theoretical and empirical literature on social capital as it pertains to economic development issues, with a particular focus on its significance for OECD countries. In so doing it seeks to address three specific questions: (1) How are social capital, human capital, and social capability related to one another?; (2) How can social capital be measured?; and (3) How might existing economic growth models give more adequate attention to social capital? The paper proceeds as follows. I begin by examining the remarkable resurgence of interest in the social dimensions of development in general, and the idea of social capital in particular. This is followed by a basic primer on social capital and a brief survey of the empirical evidence in support of key hypotheses pertaining to economic development, especially the relationship between informal and formal institutions and their collective capacity to manage risk. Next, I provide a response to several of the criticisms leveled at social capital. I then explore the implications of a general theory of social capital for economic growth and well-being
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.006 | 0.024 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".