8. Cross-Racial Refugee Fiction: Dionne Brand’s What We All Long For
Bibliographic record
Abstract
Narrative, or storytelling, is central to the process of claiming refugee status in any modern political system that abides by the UN's 1951 Convention Relating to the Status of Refugees; indeed, the asylum seeker often has nothing but their story on which to base their claim for asylum, since they usually flee their home country without documents or other forms of evidence to support their claims.The ability for refugees to narrate their stories in a hearing before a refugee board panel has been an integral part of the refugee determination process in Canada since 1985, but such storytelling has never been a simple process of narrating "the facts."Marita Eastmond insightfully explains the relationship between truth/facts, experience, and representation in the refugee's narration process, dividing it into parts: "life as lived, the flow of events that touch on a person's life; life as experienced, how the person perceives and ascribes meaning to what happens, drawing on previous experience and cultural repertoires; and life as told, how experience is framed and articulated in a particular context and to a particular audience." 1 She stresses that "What is remembered and told is also situational, shaped not least through the contingencies of the encounter between narrator and listener and the power relationship between them," 2 so much so that it may be impossible to ever reconstruct "life as lived."As Carrie Dawson points out, the many impediments to a claimant narrating her story "include language barriers, the difficulties of testifying to trauma, cultural and gendered injunctions against speaking about the source of that trauma, the inquisitorial nature of hearings, and the prescriptive nature of the written submission upon which the hearing is based [in Canada]." 3 Such barriers not only impede the claimant who is expected to produce 8 Cross-Racial Refugee Fiction: Dionne Brand's What We All Long For donald goellnicht
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".