Networks, Boundaries and Social Capital: The Historical Geography of Toronto's Anglo Elites and Italian Entrepreneurs, 1900-1935
Bibliographic record
Abstract
This dissertation examines how social inequalities are reinforced over time and in place by addressing a central question: How are power relations maintained and reproduced in space? I outline ways in which social networks contributed to the reproduction of social and economic power in early twentieth-century Toronto. I also pay particular attention to the ways in which particular spaces acted as a nexus for the reproduction of power and unequal social relations. My research captures the dynamism and complexity of social capital networks that stretched across space. These networks demonstrate that Toronto’s Anglo elite and Italian entrepreneurs lived in a world where persons interacted over a number of regions and scales. \n\tThis study contributes to the body of knowledge in social capital, network and social boundary research. Although this dissertation is largely concerned with early twentieth-century Toronto class and power relations, the results have implications beyond this case study. This research makes a significant contribution to historical geography by providing scholars interested in contemporary power relations and social networks with an empirically rich historical perspective. This study extends previous examinations of social inequality by examining how power relations were reproduced over time and through space. I analyze how social capital can be conceptualized as set of processes that is 1) integral to the acquisition of economic capital, 2) significant in constraining the action of others by redrawing the social boundaries of class and ethnicity, and 3) critical for the building of alliances across space. This research offers a complementary method to the inequality studies of David Ward, Joe Darden, Nan Lin, Richard Harris, James Barrett, and David Harvey by historically situating questions about the reproduction of social inequality through the examination of social networks.
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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 | 0.003 | 0.005 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".