A Tale of Two Cities: Framing urban diversity as content curation in super-diverse London and Toronto
Bibliographic record
Abstract
In major cities across the world policy-makers are searching for new ways to represent and govern their increasingly diverse populations. In this paper we analyse the ways in which authorities in two global cities, London and Toronto, have drawn on corporate, public management, strategies as their principal mode of diversity governance. In both we see a shift in policy making as a conscious attempt to reframe and re-imagine cities as corporate-like structures that can be conceptualised, represented, and managed through the lens of diversity management. In both cities specific representations of the city and its populations are curated to fulfil wider policy objectives. City governments present both as iconic centres of diversity, super-diversity or hyper-diversity, that embody and represent an era of progressive globalisation and new forms of contemporary cosmopolitan living. The presence of diversity is celebrated and seen a key component of ‘success agendas’. This paper is based on empirical evidence derived from a policy-oriented research project in both cities. Policy analysis and critical discourse analysis are conducted in both cities on the basis of review of policy documents at national, local and community scales, and interviews with policy makers. The paper first frames diversity as a technology of description, where we explain how diversity has become a curation strategy in public management within the framework of growing mobility of management frameworks and shifts in framing diversity in urban policies. We will then provide a comparative analysis of London and Toronto.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.027 | 0.066 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| 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".