Khawab – A Mipsterz Collaboration: The Nexus of Muslim-futurism through Fashion, Art and Technology
Bibliographic record
Abstract
Khawab is a MIPSTERZ project that centers Muslim women at the forefront of creative identity expression by connecting with participants through their real-life experiences.Khawab (kha-wa-b) or "to dream" in Urdu, is a multi-faceted storytelling portrait series from MIPSTERZ (created by Reyhab Patel) that fictionalizes the alter-egos of Muslim women in Toronto.It aims to answer the question of what informs our imagination of a utopia and what it means to have freedom of expression.Khawab is informed by the nexus of fashion, art, and culture, and how these aspects allow us to create authentic versions of ourselves in hopes of living free.This project transforms participants' experiences into fictional alter-egos, presenting Muslim women in a space of creative liberation and inclusivity, breaking free from restrictions, and embracing a space of boundless imagination.We sought to show how everyday Muslim women in Toronto imagine their alter-egos in fictional realities of their own creation.Their alter-egos described as "alternative identities" were boundless in their existence across various realities.To bring these characters to life, we crafted character biographies and captured their stories through collaborative conversations.In a two-phase approach, we first photographed Muslim women dressed as their alter-egos.Each portrait was then enhanced using artificial intelligence (DALL•E, from OpenAI) to create a digital world backdrop from key elements and textual descriptions of the alter-egoincluding interpretations of cultural identity, occupation, familial ties, as well as responses to Islamophobia, racism, and trauma.At its core, Khawab works to heal and reclaim personhood through storytelling.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".