Autonomy, equality, and respect for difference: investigating principle-based approaches to technologically mediated reproductive contexts
Bibliographic record
Abstract
The objective of this project will be to explore how the principles of autonomy, equality, and respect for difference are formulated and applied when disability diagnostic technologies affect women's reproductive decision-making. The author will use feminist disability theory and will engage legal research methodology in order to interpret and challenge those three principles as they are presented in both bioethics and jurisprudence. Specifically, the following questions will be explored:\n - Does reproductive autonomy lead to undue maternal responsibilities, especially in instances when disabilities can be or have been diagnosed?\n - Are there tensions between reproductive autonomy and reproductive equality, specifically between reproductive autonomy on the one hand, and disability equality on the other?\n - Does the (either implicit or explicit) assumption in bioethics and law that reproductive technologies be used for the purpose of disability de-selection reflect tensions between reproductive autonomy and equality on the one hand, and the principle of respect for difference on the other?\n This research will identify the principles at the heart of discourses and disagreements on reproductive decision-making in an effort to clarify how these principles are being conceptualized, to evaluate whether there is still use for a principle-based approach, and to consider what their best instantiations would look like.
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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 | 0.025 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.112 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".