The mouseness of the mouse: The competing discourses of genetics and history in <i>White Teeth</i>
Bibliographic record
Abstract
Zadie Smith’s 2000 novel White Teeth has often been hailed as a progressive vision of a multicultural Britain. Employing the discourses of genetics and describing Smith’s use of genetics in the novel’s theme of teeth and the FutureMouse©, this paper argues that Smith’s vision of multiculturalism is made complex by genetic discourses. These discourses are contrasted with personal and familial history as the source of identity for the characters of the novel. Teeth are used metaphorically to represent the rootedness of characters and the effect that migration has on first and second generation immigrants. The text highlights the difference in certainty and uncertainty experienced by characters, contrasting the certainty of white English characters with the uncertainty in the lives of the immigrants and their families. This uncertainty is contrasted with the genetic determinism that informs the life of the FutureMouse©, as well as the lives of the second and third generation immigrants depicted in the novel and Smith’s narrative provides no easy answers to the question of whether or not one’s DNA dictates one’s place in life.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.031 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".