RACE, SCIENCE AND A NOVEL: AN INTERDISCIPLINARY DIALOGUE
Bibliographic record
Abstract
In the novel Racists by Kunal Basu (2006), two competing scientists initiate an experiment that they believe will prove which race is superior. The research subjects, one white and one black infant, are sequestered on an isolated island in the care of a mute nurse. The contest must be waged in a 'natural laboratory' with no artificial interventions and with the prospect that one will die at the hands of the other. The politics of empire, the slave trade and the advent of a new scientific way of viewing life, Darwinism, set the stage for the fictional experiment, but the ramifications of such thinking extend into the present. Coming from the disciplines of nursing, philosophy and science, we discuss how a novel can illuminate the moral dimensions of science and healthcare. The critical distance afforded by the novel provides a rich terrain for the examination of issues such as race, care and the purity of science. Despite the recent dominance of social explanations of race, science requires the examination of the differences between human beings at the biological level. The view that biology is destiny is a powerful one with dangerous consequences, especially since the belief that certain human beings' destinies are far worthier than others is a corollary of such a view. In this paper, we present the cross-disciplinary conversation, which has been facilitated by this novel. We hope this will inform ethics educators of the rich potential of using fiction as a pedagogical tool.
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.036 | 0.070 |
| Scholarly communication | 0.021 | 0.021 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.013 | 0.020 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".