Mediating Postcoloniaity in Education: Mis/Representations of Muslim Girls using Technology
Bibliographic record
Abstract
In this doctoral research, I explore how social systems and postcolonial cultural norms impact the process and outcome of digital media production created by girls who belong to ethnoracial minority groups living in low-income communities. The study was conducted as a Feminist Ethnography and feminist intervention in a Toronto school over three years, with a focus on Muslim girls in 2011-2012. The purpose of this research is to respond to both the ongoing marginalization of Muslim girls in Canadian schools and to examine how digital media production can be used to bridge ongoing divides between schools and communities in low-income urban and multicultural areas of Canada. Using digital media production to explore student experiences, I identify three topics for analysis that complicate the notion of student “voice.” In this work, I address how sociocultural structures inform the process of digital media production for racialized girls, exploring what kind of meaning can be derived from student-made media and considering how the videos and photos made by Muslim girls are framed within and informed by existing social structures, social expectations, and by the intentions and interests of adults. In addition, I also examine how student concerns over being seen and/or issues related to surveillance impact what they produce (or rather, end up not producing at all). Throughout this dissertation, I also consider how student engagement with different forms of new media and technology allow for varied behaviours and interests to be performed, offering a wider view into their lives. I conclude with a discussion of silence, addressing the importance of what was left absent in the process of making digital media with Muslim girls, and explore how these omissions relate to larger postcolonial power relations, to technology, and to media education for racialized girls in under-resourced schools and communities.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.018 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".