‘Up in the northwest corner of the city’: the city, race and locating the school
Bibliographic record
Abstract
Once the Toronto District School Board (TDSB) supported the establishment of an Africentric Alternative School, the event of becoming-school added an additional layer of spatial or locational politics. Like the years preceding the deciding vote at the TDSB, the question of where the school would be located would be become part of the race politics of the city. The politics involved in the selection of a suitable location for the school would naturally appear to be the culminating event, following the political and entrepreneurial activities identified in the preceding chapters. This chapter, therefore, appears to be chronologically, the obvious one to be a last chapter in research on policy ‘implementation’. For instance, if the school was not approved by the board, then there would be no discussion about where to locate the school. However, our argument is based upon the idea that the question of location affected the entire process of the becoming of the school rather than just at the ‘end’ of a sequential process. The question of location ‘haunted’ trustees and community members prior to any governance and policy-development activities designed to produce the school. The city of Toronto was powerfully shaped by various racial, spatial and economic factors that functioned as strong preconditions for the becoming of the school. In fact, the question about ‘where to put the Black school in the White city’ would produce strong feelings across Toronto, given its long and troubled histories with placements of non-White populations (and their placements in relation to each other). As such, we have decided to place this chapter in ‘the middle’ of the book in order to illustrate how the city powerfully influenced the becoming of the school. In this chapter we examine the event of finding a location for the school, and the connections between the ways in which the city was (and is) racialised and undergoing urban change around gentrification and ‘rebranding’ of neighbourhoods. We examine the connections between education policy, cities and the new forms of suburbia in multicultural cities. The search for a home On 2 April 2008, during a meeting of the Program and School Services Committee (PSSC), members of the Africentric School Support Committee (ASSC), including Donna Harrow, Emanuel Wanzama and Leslie Moody, presented an update on the Africentric Alternative School proposal.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.028 | 0.031 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".