Bibliographic record
Abstract
In this interesting book, Aversion and Desire: Negotiating Muslim FemaleIdentity in the Diaspora, Shanaz Khan challenges us to rethink static andfixed conceptions of Muslim women. She also points out that because minority identities are fixed, women who happen to be Muslim are oftenforced to enter social and political spaces as Muslim women. Such restraintsmake it almost impossible to create a place for progressive politics, change,and fluid identities. As an anthropologist and observer, Khan pinpoints andfocuses our attention on the situation currently facing Muslim women in theWest, particularly in Canada. As a Muslim and a woman, she has located aspace in which progressive politics and change may take place.Borrowing mainly from the work of Homi Bhabha, Khan calls for movingfrom fixed and static notions of Muslim women into what Bhabha refersto as the “third space,” from which hybrid identities can be constructed. Theauthor argues that both Islam and Orientalism, the two dominant discoursesfrom which ideas about Muslim women have been and are still articulated,have led to essentializing and idealizing our images of the Muslim woman.This is also true of feminism, which sees specific aspects of Islamic practiceas oppressive to women and, therefore, the target of change; of colonialismand postcolonialism, which reinforce those stereotypes influenced by unequalpower relationships between Euro-American and Muslim societiesand that have an imperial history currently embedded in the neocolonialforms of control of other societies; and multiculturalism, which views thecultures and religions of nonwhite people as homogenous, unchanging, andunconnected to any social, political, and historical reality. All of these lensesthrough which the “others” are viewed contribute to this essentialization.Moving beyond an extremely useful theoretical discussion structured bythe concept of hybridized identities in the third space, Khan then incorporatesa series of case studies that are categorized in a manner designed to showcasea variety of Muslim women’s attempts to construct an identity, live life, andchallenge the norms of both the wider society and of the Muslim communitiesin which they live. This is done from a third space, one that they themselvesmight not even realize that they are occupying ...
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.029 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 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".