Bibliographic record
Abstract
Prompted by a chance encounter with a colleague who had commented thatSamia Hussain was the only Muslimah she knew – in a city in which about12% of the population is Muslim –the author reached out across Canada to assemblean edited collection of autobiographical essays by Canadian Muslimahs:The Muslimah Who Fell to Earth: Personal Stories by CanadianMuslim Women. She asked them to “share their personal experiences relatingto what it meant for them to be Canadians and Muslims, to tell readers detailsabout their lives, their concerns, and their aspirations” (p. 2).Hussain made “considerable effort to reflect the diversity of Canada’s Muslimpopulation” (p. 2), recounting at a book launch how she approachedstrangers on the street to ask them to contribute. This effort, which surely ledto the inclusion of people who might otherwise have been left out, is also thesource of my only minor criticism: Inviting women who are not normally writersto write their own stories gives the book a slightly uneven quality. I wishthat Hussain had taken a stronger role as editor and tidied up those pieces thatare a bit choppy, hard to follow due to missing elements, or end abruptly withouta seeming conclusion. Of course, that is also the beauty of the collection,for writers normally already have some kind of public presence. Bringing outthe voices of ordinary Muslimahs so that readers can “meet” women theywould not otherwise meet is a gift of bridge building ...
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.039 | 0.012 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| 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".