“To lose its pain, not its intensity” : The Intensity of Affect in Dionne Brand’s What We All Long For
Bibliographic record
Abstract
Dionne Brand’s What We All Long For (2005) tests out the viability of a multicultural Toronto rooted in racial, ethnic, and national differences. On some level, the novel is a hopeful account of cross-cultural and interracial connections in its portrayal of four twenty-something second-generation immigrant protagonists who form close affective bonds. Some critical studies on the novel have thus focused on the subject of affective, affiliative, and cosmopolitan citizenship and belonging, with the city of Toronto as a backdrop to such possibility. In this paper, I seek to recalibrate the importance of affect in the novel by concentrating on a particular character: Carla, half Italian and Jamaican, who lost her Italian working-class mother to suicide when she was five, an age not young enough to forget or to not know, but old enough to remember and bear the trauma of a painful personal history. In drawing on theories by scholars who have written on affect and emotion, I will argue that the fine distinction between the two terms can helpfully illuminate Brand’s representation of the character Carla’s attempts to move beyond a traumatic past. I will thus focus on the affective relations Carla claims and transmutes, and her creative use and forging of affective encounters with both human and non-human elements available to her.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.025 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".