Social capital and aboriginal economic development: opportunities and challenges
Bibliographic record
Abstract
Two main concerns animate this dissertation. On a theoretical and methodological level, it investigates the analytical utility of social capital, a concept that has gained prominence in recent years. I examine the different levels of social capital—strong, bonding ties with primary reference groups; intermediate, bridging relations between such groups; and weak, linking networks with networks of power. Associating them to geographic scales, I reinforce the analytic framework with which social capital terminology may be employed. Moving away from the uncritical celebration of social capital as a panacea to all types of social ills, I also examine issues, such as accessibility and control of resources, historical constraints and the dysfunctional potentials of social networks themselves. At the same time, as an empirical and practical study, this dissertation explores the history and future of economic development in three Northern Ontario Aboriginal communities. Canada is among the most affluent countries in the world, ranked highly on quality of life indices. Yet its First Nations share this prosperity only to a small extent. Policies aimed at improving Native peoples' lives focus on the promotion of one economic development strategy in particular, entrepreneurship. My research examines the role social capital plays in entrepreneurial success or failure. I suggest that geographical isolation segregates individuals and communities from linking and bridging networks; reliance on bonding networks in such locales often results in limited access to financial and human resources. In places where networks extend beyond the community, larger pools of resources are accessed. The dissertation highlights, however, the potential detrimental role that such external networks can play in the daily lives of marginal communities. Analysis of the colonial legislative framework which guided twentieth century policy makers in Canada examines how assimilative policies have interfered with various levels of social capital, and the consequential effect of such interference to economic and social development. My analysis, which offers some insights into the major determinants of present-day social and economic hardships in Aboriginal communities, also suggests ways in which lessons learned in the Aboriginal framework may be transposed elsewhere. At the same time, I offer a critique of social capital theory and propose ways of refining its concepts and extending its applicability.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".