Trembling strength: Migrating vulnerabilities in fiction by Sharon Bala, Yasmin Ladha, and Denise Chong
Bibliographic record
Abstract
The Boat People by Sharon Bala, Blue Sunflower Startle by Yasmin Ladha, and Lives of the Family: Stories of Fate and Circumstance, by Denise Chong, are texts that engage with vulnerability as it relates to immigration, one of the most precarious of states or sites that Canadian literature chronicles. The abstract and concrete politics of adaptation are exemplified in these narratives of displacement, inspired by the Tamil refugee crisis of 2009–2010, the Indo-Tanzanian immigration wave of the 1970s, and the resourcefulness of Chinese immigrant families in the mid-twentieth century. These narratives effectively investigate vulnerability within spaces of interconnection, imprisonment, relation, visibility, and transformation. This paper works with their explorations of the Canadian trope of immigration as a process that moves from the vulnerability of strangeness to the vulnerability of adaptation to the vulnerability of commitment. Addressing the ways that these stages are subverted, the paper examines the extent to which migrancy and its resolution resist a “national” narrative in these texts, undercutting the prototype of success through adversity. How they model Hirsch’s “openness to unexpected outcomes” recites the complexity of their depictions of vulnerability.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.017 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| 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".