Revisiting “Speak White”: A lieu de mémoire Lost and Found in Translation
Bibliographic record
Abstract
This article traces the afterlives of Michèle Lalonde’s 1968 poem “Speak White” to explore how translation contributes to constructing, renewing and transforming it as alieu de mémoirethrough various transformative processes. The term “translation” here designates a phenomenon that includes but extends beyond the concept of translation as linguistic transfer to encompass different forms of rewriting, adaptation and remediation, foregrounding the generative aspect of the memory site as well as the tension between past and present, between alieu de départand its reinscription in a new context. Specifically, it focuses on two English translations of “Speak White” that attempt to reconstruct the poem’s subversive diglossia; Marco Micone’s 1989 poem “Speak What,” as a rewriting that takes the form of serious parody; two adaptations produced during the 2012 Quebec Student Strike, “Speak Red” and “Speak rich en tabarnaque”; and the latest incarnation of “Speak White” in Robert Lepage’s887, a theatrical production that introduces its own layers of intertemporal, intermedial and interlingual complexity. These recreations of “Speak White” reveal how alieu de mémoirecan be simultaneously anchored or re-anchored in the past while also being renewed or rerouted through translation in the present across languages, cultures, media and time.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.028 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| 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".