World on a Maple Leaf: A Treasury of Canadian Multicultural Folktales Edited by Asma Sayed and Nayanika Kumar (review)
Bibliographic record
Abstract
World on a Maple Leaf: A Treasury of Canadian Multicultural Folktales. Edited by Asma Sayed and Nayanika Kumar Edmonton: United Cultures of Canada Association, 2011. 102 pp.World on a Maple Leaf is a compilation of twenty-five folktales by twentyfour authors with the aim of fostering understanding and respect for cultural differences in the multicultural contexts of Canadian life. These stories are folktales in a generous sense of the term in that they include rewritings of published tales, oral tales from grandparents, and original compositions. The brief directive given contributors was to re-imagine . . . stories . . . heard from parents, grandparents, friends and families, and write them for Canadian (vii). The first thousand copies are for free distribution libraries; further sales will support children in need. The writers and editors show such idealism and the project is so manifestly worthwhile that any criticism may sound peevish, but from a folklorist's perspective questions arise.I had hoped, from the title, for a collection of newly recorded oral folktales from recent immigrants Canada. Surprisingly, all but six of the contributors were bom in Canada or the United States. All are highly literate, identifying themselves as storytellers (nine), writers (eight), academics (three), and graduate students (four), with three of the students studying comparative literature at the University of Alberta. I would have expected Edmonton immigrant communities have been canvassed, and perhaps they were because the introduction mentions a call that elicited overwhelming response and the painful rejection of some fascinating stories (vii-viii). It is not clear whether any of the included stories came as the result of inquiry among new immigrants. This is a pity, especially because the final contributor, Roxanne Felix, writes eloquently about the value of ask[ing] about a person's journey (94). If this is just the first in a series, as the editors hope, it will be worth going new Canadians and recording their stories directly, rather than relying on others, no matter how refined their storytelling skills, speak for them.It would surely be empowering for immigrants know that their oral literature is valued in their new country This raises my second question: Why is this compilation so liter acentric1. Although all the contributors speak highly of oral storytelling, the average reader would assume from this collection that oral tales are just an imperfect stage on the way becoming written stories. …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".