Bibliographic record
Abstract
How do diverse populations negotiate the terms according to which they live together within the shifting confines of urban spaces? How do these negotiations impact the social relations and hierarchies shaping daily living in cities? This dissertation explo res two cases of localized collective action in Vancouver, Canada. It argues that these actions exemplify a particular form of political contestation: the politics of contentious proximity. Both cases involve groups of urban residents who, in response to what they conceive as the incursion of new demographic groups and physical forms in “their” areas of the city, deploy representations of the identities associated with different types of people and places as a political tool. These groups frame themselves, their political adversaries and the relations of proximity that exist between them in specific, strategic terms. They do so both to establish themselves as legitimate political actors— actors with a right to a voice in determining the shape of the places in which they dwell—and to assert their entitlement to material and symbolic resources. This dissertation traces the representational strategies involved in both cases and the ways these strategies operate as processes of social construction. Further, it shows that the conceptualization and exploration of this politics has both practical and theoretical significance. It offers insight into the tactics residents use to respond to the changing demographic and physical forms of their cities, and, into the social and spatial exclusions on which these tactics are predicated. Moreover, it also makes two significant theoretical contributions. First, the politics of contentious proximity provides an alternative to two dominant lenses used to analyze urban contestation: NIMBY (not in my backyard) and ‘urban social movements’. Second, it contributes to the study of identity within political theory by suggesting an expanded conception of identity politics. It adds to our understanding of the political phenomenon of “identity”, not conceived as some deep primordial aspect of one’s individual being, as it is commonly understood, but as something actors employ and deploy, for particular and, in the cases of the actions this dissertation explores, localized, political purposes within neighbourhoods.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.050 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".