Stories of Immigrant Isolation and Despair: Canadian Novels and Memoirs Since the 1850s
Bibliographic record
Abstract
In The Female Malady published in 1985, Elaine Showalter notes that representations of madness in literary texts are not simply reflections of medical and scientific knowledge, but are part of the fundamental cultural framework in which ideas about insanity are constructed. Writing from a feminist perspective on women, madness and English culture, Showalter draws extensively upon women’s diaries, memoirs, and novels in order to include women’s voices as well as the male views set out in medical literature. One of her first examples is Bertha Mason, the madwoman in the attic in Charlotte Bronté’s well-known novel, Jane Eyre . While Showalter describes Bertha’s violence, sequestration, and regression to an inhuman condition as a powerful model of female insanity for Victorian readers, she never mentions Bertha’s Jamaican immigrant background. Yet ethnic identity, or the immigrant experience, is a vital aspect of the cultural framework that literary texts both reflect and help to shape and is thus significant in an understanding of ideas about insanity. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.064 | 0.030 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".