Bibliographic record
Abstract
Towards the end of Rohinton Mistry's story Swimming Lessons, the protagonist's parents reflect on the merits of a book written by their son and decide that he will be a successful writer only if he continues write about his recent experience as an Indian immigrant in Canada, because, they argue, Canadians interested in reading about life through the eyes of an immigrant. They worry, though, that he will become so much like them that he will write like them and lose the important difference. Mistry's story is funny, but his suggestion that ethnic minority writers are encouraged and expected reproduce recognizable images of ethnic difference is serious and is echoed by a number of contemporary Canadian writers, including, for example, Tom King, Dionne Brand, Eden Robinson, and Fred Wah. Coercive mimesis is the name that Rey Chow gives the dynamic described by these writers, all of whom suggest that they have been asked, in one way or another, to resemble and replicate the very banal preconceptions that have been appended them. Focusing on the work of poet and essayist Fred Wah, this paper considers how the idea of fakery or faking it can be used undermine the dynamics of coercive mimesis.
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 | 0.013 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.067 |
| Scholarly communication | 0.018 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".