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Record W7126154618 · doi:10.3138/md-68-4-1410

“A Leasing of Bodies”: Race, Medicine, and Radical Care in Jamie Lloyd’s Revival of Lucy Prebble’s <i>The Effect</i>

2025· article· en· W7126154618 on OpenAlexvenueno aff
Hayley Wilson

Bibliographic record

VenueModern Drama · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicPostcolonial and Cultural Literary Studies
Canadian institutionsnot available
Fundersnot available
KeywordsContemplationDiasporaBlack womenLived experienceMedical careOral history

Abstract

fetched live from OpenAlex

British director Jamie Lloyd’s 2023 revival of Lucy Prebble’s 2012 play The Effect re-frames the play’s contemplation of love and medicine in racialized terms. The Effect follows two volunteers who fall in love during a drug trial and two doctors who monitor them. Featuring an all-Black cast, Lloyd’s revival evokes the troubling history of medical experimentation on Black bodies while also modelling ethical and racially conscious care. As the Black cast was encouraged to bring personal aspects of their own lives into the production, Prebble’s original text is moulded to the myriad lived experiences of Black persons in the diaspora in the 2023 revival. By using Christina Sharpe’s theory of the wake and Harriet Washington’s history of medical apartheid, applied on a broader, global diasporic scale, this article traces key moments in the revival that were tailored to the Black actors’ positionality. These approaches open The Effect to further discussions of race, medicine, and the practice of a radical, ethical model of care that is deeply human.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.050
Threshold uncertainty score0.099

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0230.049
Scholarly communication0.0100.005
Open science0.0010.006
Research integrity0.0050.015
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.012
GPT teacher head0.243
Teacher spread0.231 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations0
Published2025
Admission routes1
Has abstractyes

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