Old/New Stories – Old/New Genre: Canadian Historio-graphic Ethnofiction Revisited in Pik-Shuen Fung’s Ghost Forest
Bibliographic record
Abstract
Canadian literature has been marked by a type of writing, which grounded in historiographic recollections of immigration and (ethnic) identity, has been generally classified as ethnic (minority) or diasporic writing. Yet, in the 1990s, Janice Kulyk Keefer, Ukrainian-Canadian writer, suggested the term “historiographic ethnofiction” which offers much more than the customarily applied generic labels as it captures the richness of works discussing ethnicity. Thus, the concept, so far unattended by critics, deserves attention as a productive tool of generic classification that sheds new light on how ethnic writing could be approached in a more systemic way. To illustrate its potential, this article aims at reading a recent Asian-Canadian work, Ghost Forest (2021), by Pik-Shuen Fung, as an example of contemporary Canadian historiographic ethnofiction that both follows and extends Kulyk Keefer’s conceptualization of this generic term.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.048 | 0.029 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".