Creating diversity capital: transnational migrants in Montreal, Washington, and Kyiv
Bibliographic record
Abstract
do urban communities accommodate this century's massive transnational migrations? This volume seeks clues about how a city's capacity for urban social sustainability, termed diversity capital, may expand under such conditions. author, Blair A. Ruble, examines three cities, now receiving large numbers of new immigrants, that have long histories of division into just two communities of language and race: Montreal, Washington, and Kyiv. The growing presence of individuals who do not fit into long-standing group boundaries fundamentally alters the social, cultural, and political contours of traditionally bifurcated metropolitan regions, writes Ruble. How does that presence change perceptions and institutions? Creating Diversity Capital approaches this topic in terms of how the new immigrants live, work, and go to school and describes how the politics in each of these cities has changed, or failed to change, in the face of the new demographics. A special feature is the use of important new information on Kyiv from a set of surveys conducted by the Kennan Institute in 2001-2
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".