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Record W3092142029 · doi:10.35632/ajis.v34i3.793

The Muslimah Who Fell to Earth

2017· article· en· W3092142029 on OpenAlexaffabout
Katherine Bullock

Bibliographic record

VenueAmerican Journal of Islam and Society · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Islamic Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFellDiversity (politics)BeautyCriticismPopulationMinor (academic)HistoryInclusion (mineral)SociologyPsychologyMedia studiesGender studiesAestheticsLawPolitical scienceArtDemographyGeography

Abstract

fetched live from OpenAlex

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 ...

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.147
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.017
GPT teacher head0.337
Teacher spread0.320 · 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 designQualitative
Domainnot available
GenreEmpirical

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 routes2
Has abstractyes

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