Performing gender, race, and class through the Barbie Expo
Bibliographic record
Abstract
In this presentation, we use the lens of public pedagogy and the practice of feminist hacking to explore the Barbie Expo, a large-scale ‘exhibition’ curated at Les Cours Mont-Royal shopping centre in central Montreal. We detail how it problematically performs gender and its intersectionalities. Moreover, housed in commercial space, we illustrate how the Barbie Expo connects women directly to consumption. We will begin by grounding the Barbie Expo in discourses of public pedagogies and gendered exhibitionary practice, in particular, identity formation and the male gaze, and then trace briefly the complex history of Barbie . We then discuss the Feminist Museum Hack , a methodology we adapted to analyse the visual discourses of the Barbie Expo and how they promote and maintain gendered and other hegemonies. We present our findings thematically, around intersecting performances of gender, race, and class, to illustrate how the Barbie Expo privileges whiteness, innocence, exotic otherness, and traditional female roles, all wrapped up in a pretty, fashionista, capitalist bow.
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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.031 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".