Bibliographic record
Abstract
The starting point of this project was the ‘paradox’ in how the concept of urban diversity is evoked, in theory, in policy and in practice, as something which is simultaneously celebrated and demonised. Diversity is indeed a fashion word, it sounds celebratory, tolerant and harmonious, but not too confrontational (Essed, 2002). Diversity has gained popular appeal especially because it offers an approach that goes beyond ‘dated’ terms such as equality and anti-racism. Yet diversity workers often tend to experience this very paradox, working within organizations that claim to be committed to diversity but feeling as though they are ‘banging their head against a brick wall’ (Sara Ahmed, 2012, emphasis mine). The same paradox is evident in the manner in which the city of Toronto approaches its diversity. The premise that diversity is a strength which should be celebrated appears to be a popular notion within Toronto’s city policy and mainstream public discourse. Yet, Toronto’s most diverse neighbourhoods located at the edges of the city are scapegoated and criminalised. This is especially the tendency when ethnic, cultural and religious diversity coincide with poverty, welfare dependency and poor infrastructure. This study set out to provide empirical knowledge of what living with and working towards diversity in urban areas looks like. Specifically, it raised the question: How is diversity experienced at the neighbourhood level, as (a) discourse, (b) social reality, and (c) practice?? This question was broken down to four sub-questions which were investigated in four interconnected chapters (chapters 3 to 6). The present concluding chapter provides a summary of the findings of each empirical chapter and further discusses these findings in relation to one another. It closes with recommendations for both policy and future scholarship addressing diversity in our cities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.428 | 0.093 |
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".