Breaking the Linguistic Minority Complex through Creative Writing and Self-Translation
Bibliographic record
Abstract
Generally speaking, a minority language is “one spoken by less than 50 percent of a population in a given region, state or country” (Grenoble and Singerman, 2017, n.p.). In this article, I propose a more contextualized definition that applies to the realm of literary writing and (self-)translation. Thus, I define a minority language as any language which a bilingual or plurilingual writer perceives as not being the dominant one in the sociocultural and linguistic context in which s/he is active as an author or as a (self-)translator. Assuming this alternative definition as a point of departure, I discuss the creative and self-translational practice of the Canadian writer Antonio D’Alfonso. D’Alfonso is one of those rare plurilingual writers who feel linguistically defamiliarized, claiming that instead of having a proper mother tongue he has a mixed baggage of native Molisano dialect, French, English and Italian. Thus, he tends to write, think and (self-)translate immersed in a kind of 3D- (or even 4D-) linguistic landscape (Pivato, 2002). D’Alfonso’s self-translations from French into English and/or vice versa are testimony to the author’s experimental way of challenging the “crude subjugation” (Whyte, 2002, p. 69) of a language over another and of overcoming any minority-language complex he might have developed on his path to becoming a linguistically uprooted writer.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.032 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".