Small Cities, Big Issues: Reconceiving Community in a Neoliberal Era
Bibliographic record
Abstract
Small Canadian cities confront serious social issues as a result of the neoliberal economic restructuring practiced by both federal and provincial governments since the 1980s. Drastic spending reductions and ongoing restraint in social assistance, income supports, and the provision of affordable housing, combined with the offloading of social responsibilities onto municipalities, has contributed to the generalization of social issues once chiefly associated with Canada’s largest urban centres. As the investigations in this volume illustrate, while some communities responded to these issues with inclusionary and progressive actions others were more exclusionary and reactive—revealing forms of discrimination, exclusion, and “othering” in the implementation of practices and policies. Importantly, however their investigations reveal a broad range of responses to the social issues they face. No matter the process and results of the proposed solutions, what the contributors uncovered were distinctive attributes of the small city as it struggles to confront increasingly complex social issues. If local governments accept a social agenda as part of its responsibilities, the contributors to Small Cities, Big Issues believe that small cities can succeed in reconceiving community based on the ideals of acceptance, accommodation, and inclusion.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.025 | 0.027 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".