Ghosts of eugenics’ past: ‘Childhood’ as a target for whitening race in the United States and Canada
Bibliographic record
Abstract
While in modernity childhood was increasingly invested with emotional and intellectual energy, it also became a site of scrutiny and intervention, so that philosophers, scientists, and humanitarians pursued the improvement of humanity and the human condition through management of ‘the child’. In the first half of the twentieth century, such attention settled on children deemed to present both problems and opportunities for the improvement of the race, as eugenics came to dominate discussions of human progress. This article examines the significance of childhood as a resource for human futures and technologies of ‘eugenics’ insofar as they target children: specifically, the development of intelligence testing, institutions of separation, and involuntary sterilization in the United States and Canada. In these discourses and technologies of eugenics, childhood appears as a reserve of human potential which, appropriately regulated, may be harnessed to ‘build a better future’. The article also considers the perspective of survivors of these practices who experienced their childhood and future possibilities as having been expropriated from them by the state. By considering these governmental and personal registers side by side, the article sheds light on the perceived social utility of childhood, as well as the particular character of loss experienced by those whose childhoods were subject to state intervention.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.038 | 0.039 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".