An autonomous place in the global city: an interpretation of Dionne Brand’s “What we all long for”
Bibliographic record
Abstract
Dionne Brand creates characters with peculiar aspirations in her novel What we all long for. In Toronto, Quy, Tuyen, Carla, Oku and Jackie have their childhoods and origins shaped by the urban spaces they occupy. The author elaborates the struggles between immigrants in Canada and their children, born in the country, as these young people drift in Toronto. How much are these tensions natural in child-parent relationships? In what ways are they more difficult because of the immigrant experience? In this paper, we focus on Oku to point out that the strain in child-parent relationships resembles the tensions between the diasporic subjects and the global city. We conclude that Oku became more aware of his potential to act upon the development of his identity. The main concept that guided this interpretation was that of reterritorialization, as proposed by Kit Dobson (2006).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.036 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.005 |
| 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".