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Record W2994067353

Book review: Beverly J. Rasporich. 2015. Made-in-Canada Humour: Literary, Folk and Popular Culture. Amsterdam: John Benjamins. Hardbound. 300 pp. ISSN 2212-8999

2017· article· en· W2994067353 on OpenAlexaboutno aff
Ioana Ciurezu

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicMedia, Gender, and Advertising
Canadian institutionsnot available
Fundersnot available
KeywordsClichéContext (archaeology)Identity (music)LiteratureThe artsHistoryMedia studiesArtSociologyAestheticsVisual arts
DOInot available

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.177
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0030.006
Open science0.0040.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.000

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.

Opus teacher head0.138
GPT teacher head0.507
Teacher spread0.369 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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