Bibliographic record
Abstract
Abstract Human progress has occurred throughout human existence. It is typically regarded as a positive thing in as much as it proceeds on the basis of, and results in, changes to the human condition that we value. Thus, changes associated with progress occur since they are judged, using current moral standards, to be “good” things and the “right” things to do. Eugenics, the practice of purposely bettering society by influencing its genetic makeup, was a philosophical and practical tool used by leaders of European societies and the countries under their influence during the final two decades of the 19th and the first half of the 20th centuries. This was not entirely a new idea, however, as ways of purposely manipulating genetics of plants, animals, and humans have been practiced before recorded human history. The practice of eugenics went “too far” by Nazis during World War II for international moral standards and was quickly abandoned in favor of an international emphasis on human rights. Still, eugenics practices continued and continue to the current time. Today, we are faced with the question of whether or not this is a prudent path to follow, especially given that progress in medical and genetic fields is expanding at a very rapid pace. It is suggested here that rapid progress in these areas in the future may make any current practices of the new eugenic not only outdated, but also irrelevant. Thus, it seems unwise to follow new eugenics practices in a whole‐hearted way.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.048 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.007 | 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".