Book review: Beverly J. Rasporich. 2015. Made-in-Canada Humour: Literary, Folk and Popular Culture. Amsterdam: John Benjamins. Hardbound. 300 pp. ISSN 2212-8999
Bibliographic record
Abstract
Made-in-Canada Humour is a journey through space and time in Canadian humour. Rasporich, Arts Professor at the University of Calgary, masterly creates a general picture of Canadian humour culture, thus revealing its particularities. What I particularly enjoyed about this research is the fact that the story line is easy to follow. The author structured the chapters geographically, leading the reader through Canadian humour from East to West. The strong point of the research lies in the large amount of examples provided, thus becoming a useful tool for scholars who study Canadian humour in particular, but also for those who want to better understand the Canadian culture. Made-in-Canada Humour is an analysis of the way in which humour was understood in the 19th and 20th centuries. As she stated from the beginning of the book, Rasporich wrote it with the intent of recording cultural history, rather than developing humour theories. The author claims from the beginning that the issue she addresses is whether Canadian cultural identity revolves around ‘not being American’. Rasporich is intrigued by the cliché that Canadian cultural identity is more or less invisible. In this context, beginning with the study of literary humour and ending with the analysis of the forms of folk humour and popular culture, the author tries to establish to what extent humour and culture interact.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.075 | 0.066 |
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".