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Record W4409033419 · doi:10.1093/adaptation/apaf004

The artist biopic and posthuman feminism: <i>Maudie</i> (2016) as ‘bio-zoe-geo-pic’

2025· article· en· W4409033419 on OpenAlexaboutno aff
Katrijn Bekers

Bibliographic record

VenueAdaptation · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsnot available
Fundersnot available
KeywordsPosthumanFeminismArtBiographyArt historySociologyAestheticsGender studies

Abstract

fetched live from OpenAlex

Abstract This article discusses Aisling Walsh’s 2016 biopic about disabled Canadian folk artist Maud Lewis, Maudie, as a ‘posthuman feminist biopic’. Reading the film through a posthumanist lens with reference to concepts such as posthuman feminism (Rosi Braidotti), transcorporeality (Stacy Alaimo), vibrant matter, thing power, assemblage (Jane Bennett), and companion species (Donna Haraway), it argues that the portrayal of Maud diverges from traditional artist biopics. While artist biopics usually strongly hold on to humanist values such as individuality and self-sufficiency, Maudie foregrounds ideas of relationality and collectivity. It emphasizes how the human (an artist in this case) is intertwined with the more-than-human-world and inseparable from its surroundings. The film creates an equilibrium between human and non-human elements, showing how the artist is embedded within the material realm. It portrays ‘things’ not as passive objects but as actants, suggesting that things, humans, and non-human animals are in constant relationship with each other. It reframes art not just as an individual human act but as the result of multiple human and non-human interactions. The article concludes that Maudie revises not only the conventions of the artist biopic subgenre but also opens up reflections on the term ‘bio-pic’, suggesting that it needs to be rethought to include the idea that humans are in constant relationship with each other, the non-human and the world. In this light, it proposes the term ‘bio-zoe-geo-pic’.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.808
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.316
Teacher spread0.299 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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