I would rather be a cyborg than a Barbie: hyperfeminine nostalgia and patriarchal futurity in <i>Barbie</i>
Bibliographic record
Abstract
I examine portrayals of digital media and technology in Barbie, analysing Information and Communication Technology (ICT) symbolism in conjunction with the film's thematic commentaries on gender and contemporary affects. I draw my theoretical framework from Tracey Mollet's conceptualisation of twenty-first-century American nostalgia and Donna Haraway's iconic discussion of the cyborg, tracing all appearances of digital screens and devices, media franchises, computer software and virtual platforms in the film. I demonstrate that Barbie consistently associates ICT with toxic masculinity and corruptive futures, while disassociating desirable feminine spaces and interactions from technological artifacts and their use. This gendered representation of technology is contextualised within two competing nostalgias portrayed in the film: a hyperfeminine, commercialised girlhood and a masculine, nationalist and patriotic past. Analysing the strategic placement of digital ICT across the film's settings – Barbie Land, the Real World and Kendom – I illuminate the paradoxical relationship between the film's selectively feminist message and its dichotomous portrayal of technology. This case highlights the challenges of portraying feminism without stakes in popular culture, evoking Sarah Banet-Weiser's observations of neoliberal girl-power narratives in the guise of feminism. Ultimately, I argue that the portrayal of technology in Barbie reinforces biological essentialism, revitalising archaic logics which fixate on the physical gendered body as the motivator for feminist solidarity; moreover, it overlooks the importance of digital spaces in feminist praxis and theory, complicating its status as a pop feminist film.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.025 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".