Haunting Biology : Book Forum
Bibliographic record
Abstract
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.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.081 | 0.030 |
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".