Settling down and Settling Up: The Second Generation in Black Canadian and Black British Women’s Writing
Bibliographic record
Abstract
Settling Down and Settling Up discusses five works by black Canadian and black British authors published between 1998 and 2005 (Tessa McWatt’s Out of My Skin, Andrea Levy’s Fruit of the Lemon, Zadie Smith’s White Teeth, Esi Edugyan’s The Second Life of Samuel Tyne, and Dionne Brand’s What We All Long For), chosen both for their concern with the processes of settlement of black families (“settling down”) and for their engagement with the conditions of citizenship that the second generation encounter in those two countries and aim to renegotiate (“settling up”). Whereas, in isolation, each and every one of these novels has received substantial critical attention to date, what makes this book highly original is the fact that they are brought together into a comparative framework for the first time. It must be noted here that, whereas black British/Canadian writers are relatively often compared with their African American peers, they are seldom connected to each other by critics. In this ground-breaking study, however, the analysis of each novel is used to illuminate the others following neither a chronological order based on the date of publication nor a geographical one. On the contrary, chapters switch from one to the other quite seamlessly even as a new concept or aspect of the topic is introduced, starting with the latest work, Brand’s What We All Long For, and ending with Smith’s first novel.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.055 | 0.017 |
| Scholarly communication | 0.015 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".