Bibliographic record
Abstract
Racial Attitudes in English-Canadian Fiction is a critical overview of the appearances and consequences of racism in English-Canadian fiction published between 1905 and 1980. Based on an analysis of traditional expressions in literature of group solidarity and resentment, the study screens English-Canadian novels for fictional representations of such feelings. Beginning with the English-Canadian reaction to the mass influx of immigrants into Western Canada after World War One, it examines the fiction of novelists such as Ralph Connor and Nellie McClung. The author then suggests that the cumulative effect of a number of individual voices, such as Grove and Salverson, constituted a counter-reaction which has been made more positive by Laurence, Lysenko, Richler and Clarke. The “debate” between these two sides, carried on in fictional and non-fictional writing, is seen to be in part resolved in synthesis after World War Two, as attitudes are forced by wartime alliances and intellectual pressures into a qualified liberalism. The author shows how single novels by Graham, Bodsworth, and Callaghan demonstrated a new concern for the exposure and eradication of racial discrimination, an attitude taken further by the works of Wiebe and Klein. The book concentrates on single texts that best portray deliberately or not, racist ideology or anti-racist arguments, and attempts to explain the arousal in Canada of such ideas.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".