Bibliographic record
Abstract
The article analyses Patricia Grace's novel Baby No-Eyes (1998) in light of debates about the Human Genome Diversity Project and its research on indigenous communities. Focusing on the story of a miscarried Māori baby whose eyes are removed for medical testing, Grace highlights the continuities between extractive colonial practices such as land dispossession and new, biocolonial activities regarding the mining of the human body. Her dramatization of culturally fraught medical encounters contributes to debates about cultural safety in healthcare, while the novel's exploration of indigenous genealogy, ghosting, and health advocacy challenges the HGDP's language of vanishing communities and extinction. Baby No-Eyes asserts powerful arguments for more robust ethical protocols in genetic research, humanizing debates that often take place on the level of bioethics, health policy, and international law. The article concludes that the novel's ethical recommendations provide conceptual foundations that cou...
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.045 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".