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Record W4411378438 · doi:10.17157/mat.12.3.10020

Haunting Biology : Book Forum

2025· article· en· W4411378438 on OpenAlexaff
Trevor Engel, Beth Greenhough, Frédéric Keck, Meredith Evans, Benjamin Hegerty, Emma Kowal

Bibliographic record

VenueMedicine Anthropology Theory · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics, Bioinformatics, and Biomedical Research
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
Fundersnot available
KeywordsBiologyLibrary scienceComputer science

Abstract

fetched live from OpenAlex

Emma Kowal’s Haunting Biology: Science and Indigeneity (2023) investigates the history of biological and medical research about Indigenous peoples in Australia. This book forum invited contributors to provide nuanced insights that engage the book’s central contributions to debates in medical anthropology about decoloniality and racial science. Bringing together medical historians, anthropologists, and scholars of science and technology Trevor Engel, Beth Greenhough, Frederic Keck, and Ros Williams, the forum’s contributors highlight the profound utility of Kowal’s insights and the necessity of attending to the spectral presence of the colonial-era ghosts that haunt the ground on which contemporary biological science, including genetics and epigenetics, is practised. The forum contributors draw out the multivalent affects that ghosts provoke, brought to presence through Kowal’s ethnographic observations and rich archival research. They engage ghostly characters like British scientist Baldwin Spencer, who sits out of sight but not out of mind in a museum storeroom, and surgeon and Australian anatomist Sir William Colin Mackenzie, who haunts the dreams of Goenpul Indigenous filmmaker Romaine Moreton. Each contributor shows the productive tension gained by following Kowal’s directive to listen to these and other ghosts around us, and gesture towards the possibilities of decolonial scientific practices.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.856
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.004
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.337
Teacher spread0.326 · 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 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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