Nationalizing Shakespeare in Québec: Theorizing Post-/Neo-/Colonial Adaptation
Bibliographic record
Abstract
Québec's political situation and multiple identities as a colonial, postcolonial, and neo-colonial nation make its adaptations of Shakespeare unique. By appropriating the canonical authority of Shakespeare's texts, Québecois adapters legitimize their local struggle for national liberation; however, this appropriation requires that they negotiate a fine line between the enrichment of Québecois culture and its possible contamination, assimilation, or effacement by Shakespeare's influence. This article proposes three reasons why Québecois playwrights choose to adapt Shakespeare more often than Molière: the indeterminacy of Shakespeare's texts; his "big time" status; and Québec's cultural distance from the British canon. These factors result in Québeccois playwrights' irreverent, and hence liberating, approach to "le grand Will." Québec's overlapping post-/neo-/colonial identities make its relationship to Shakespeare distinct from that of English Canada. While Mark Fortier claims that Canadians are "undead" due to their ambivalence as settler-colonizers "from elsewhere," I argue that in Québecc the national question eclipses forms of alterity in these adaptations of Shakespeare. Québecois adaptations tend to be oriented towards the creation of one multiethnic, national identity to which "others" must assimilate as the nation strives collectively for political sovereignty and legitimacy.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.023 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".