Bibliographic record
Abstract
Introduction and Definition: Eugenics is the belief that the human species can be "improved" through selective reproduction, derived by Francis Galton in 1883. This paper presents a critique of eugenics as a practice in Canada, and how it has evolved over time to represent an autonomous approach in current reproductive healthcare. This historical progression is examined by outlining policies and comparing practices in eugenics that have shifted how current reproductive healthcare is conducted. Origins of Eugenics: During the 1930s, physicians adopted eugenics and called for the sterilization of those considered "defective” including the lower class, immigrants, ethnic minorities including Indigenous groups, LGBTQ+, criminals, and those with disabilities. Eugenicists argued that controlling heredity was essential to eliminate the unfavourable traits. It is important to note that desirable and undesirable traits varied by country. The country’s eugenic policies depended on which traits were considered undesirable and desirable and ultimately led to sterilization. Policies and Practices in Canada: In Canada, government intervention focused on shaping the genetic makeup of society through laws and policies granting the state control over procreation. Alberta was the first province in Canada to introduce the Alberta Sexual Sterilization Act in 1928. According to the Act, sterilization would prevent "the risk of passing “mental defects” to future children and was often used for individuals who were institutionalized for mental health issues. Modern Eugenics in Healthcare: Current research in genetics focuses on the use of reproductive technologies to guide reproduction decisions by prioritizing individual choice and informed consent using genetic screening and counseling. For example, prenatal screening detects genetic defects and improves parental reassurance and pregnancy management. However, challenges remain regarding the implications of identifying and addressing genetic abnormalities prenatally. Implications of Eugenics in Healthcare and Future Directions: In healthcare, initiatives such as sterilization or selective termination were used to reduce future health burdens. Modern geneticists sought to differentiate their work from the eugenics movement by prioritizing reproductive autonomy in their practice. As a result, the emphasis on bioethical principles such as autonomy and justice in medicine have introduced the role of genetic counselling in perinatal care.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.062 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.014 | 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".