Writing the gap : the performance of identity in texts by four Canadian women
Bibliographic record
Abstract
This examination of the writing by four Canadian women takes common notions of identity to task. Investigating the strategies that Lee Maracle, Joy Kogawa, Dionne Brand and Gail Scott use in their texts, this work builds an argument for a positing of identity as a kind of assemblage. Re-configuring identity as an activity or performance rather than an inborn immutable trait empowers typically-disadvantaged groups to remake their worlds by re-making their identity. -- The importance of language as shaper of culture emerges as the examined texts manifest women characters who creatively seize control of their lives. They become agents of change by entering language and wrestling with its ambiguities. These writers insert markers, codes and signs of identity into gaps and spaces in traditional forms, breaking open codified patterns. Deft, flexible, adaptive and determined, women in these texts form a bricolage of signifiers and imbue them with the potency of identity. -- Language as a bodily act, the reclamation of sexual power, an exploration of the effects of hate speech, and interrogation of racist, sexist and classist paradigms all work in these selections to support the necessity for a new understanding of identity. Specific techniques such as the trace, the transverse, the genotext, and the deployment of certain positivist values enable the writing to re-invent the nature of identity.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.058 | 0.019 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".