Gender Construction in Muslim Tweens Stories : A Discourse of Intersectionality of Religious and Gender Representations
Bibliographic record
Abstract
The following study set out to examine the creative works of five Muslim tweens in Toronto, Canada, with focus on analysing the intersectionality of religious and gender representations in their works. Theoretical framework underlining this study is a discourse on visual representation of female Muslim characters, hybrid construction of gender, religious values, and media consumption. The primary research questions of this study are; (1) How do Muslim tween girls reproduce meaning and construct gender identity in their creative works? (2) How do their stories intersect gender construction with their religious background and media consumption? The results of this study revealed the hijab (Muslim head scarf) as significant visual representation of female Muslim characters in young adults’ stories. It affirms hybrid representation of gender, religious and media consumption which, in turn demonstrates Muslim tweens mitigation in gender construction. This study also reveals the fluidity of domination which explores aspects such as new context of non-existent male-characters, religious identity and kindness as the indicator of perceived beauty. Additionally, some of these tweens associate feminine identity and representation with nature which is deeply rooted in Western fairy tales and religious values (Judeo-Christian and Islam).
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.015 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".