Bibliographic record
Abstract
This chapter discusses Esi Edugyan’s exploration of narrative structure, viewpoints, and aesthetics in her fiction and places her work in the context of second-generation writing in Western Canada. It examines the multilayered exploration of Black history in her works through the identification and analysis of two overarching concerns: the notion of Black fugitivity and the related possibility of a second life. It argues that the writer has displayed to date a strong interest in tracing throughout different geographies and chronologies those forms of struggle and resistance against anti-Black hatred understood as “fugitive” because they may remain unacknowledged or hardly visible. The male protagonists of Edugyan’s novels experience harrowing loss and suffering due to the social conditions of Black citizenship in each setting and period, which typically define Black life as less than human. Yet, the fictions offer hope for affective healing and new beginnings in a second life, as well as the possibility of reparation and redemption after wrongdoing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 0.010 |
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; both teacher heads agree on what is shown here.
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".