THE IDENTITY CRISIS OF RETA WINTERS: GENDER AND ETHNICITY IN CAROL SHIELD’S UNLESS
Bibliographic record
Abstract
Carol Shield’s novel Unless (2002) mainly revolves around Reta Winter’s identity crisis in comparison to her past and present self. As an activist in her youth and a current writer, Reta’s world turns upside down after the news that one of her daughters, Norah, unexpectedly decides to live in the street. This incident reminds Reta how much she is estranged from her past self in the journey of self-discovery and how it leads her into an identity crisis. In this research paper, it will be discussed how Reta’s life changes after her daughter Norah’s choice, how Alicia, one of the protagonists in Reta’s novel, changes her life choices dramatically, and how Canada’s multiculturalism policy is presented in life. The interconnectedness among Norah’s choice of living in the street by sitting behind a cardboard sign “goodness” (after experiencing a traumatic incident), the references related to Norah’s clothes and where she chooses to sit, and the relationship between Norah’s traumatic event and Canada’s multiculturalism policy will lead Reta to revisit her past self that she has forgotten for a very long time and will guide her to remember her old self as the other. Reta’s personal trauma and Canada’s cultural trauma will be analyzed in the scope of literary trauma theories by the contribution of the pioneers in this field, particularly Cathy Caruth.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.039 | 0.022 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| 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".