Bibliographic record
Abstract
Abstract What will the demands of distributive justice be in the post‐genetic revolutionary world? Will genetic inheritance be regarded as socially distributed goods? This may seem a more reasonable position to assert as biotechnology progresses further toward human genetic manipulation. Advances in human genetics raise a number of unique considerations for theories of justice, ranging from the realisation of egalitarian ideals and the therapy/enhancement distinction to the scope and limits of reproductive freedom. As new empirical discoveries are made concerning the environmental and natural determinants of human welfare, theories of justice must re‐conceptualise what the demands of justice are and how society can fairly distribute the natural and social goods which influence the life prospects of humans. Key Concepts: Aging is the progressive loss of function accompanied by decreasing fertility and increasing mortality with advancing age. Distributive justice is concerned with what constitutes a fair distribution of the benefits and burdens of social cooperation. Luck egalitarianism is the view which maintains that inequalities that are the result of factors beyond a person's control, such as inequalities in natural endowments, are unjust. The Priority View maintains that it is morally more important to benefit the people who are worse off. Procreative liberty is freedom in activities and choices related to procreation. The Sufficiency View maintains that what is morally important is for everyone to have enough.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.033 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| 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".