The flaws of the expressivist objection: an inadequate argument against prenatal testing and/or disability-selective abortion
Bibliographic record
Abstract
The expressivist objection claims that the use of prenatal testing and/or disability-selective abortion results in the expression of negative and/or discriminatory messages to people with disabilities. From this argument, one can deduce a claim that it is morally wrong to use these technologies. These technologies are inextricably linked to both the rights of people with disabilities and the rights of women to reproductive autonomy. This tension is explored, and four flaws of the expressivist objection are examined: 1) its claim that the aforementioned messages expressed are discriminatory; 2) its presumption that prospective parents’ motivations for using these technologies concern only the future child’s life, and its consequent oversight of prospective parents’ concerns regarding their own lives as caregivers of a child with a disability; 3) its reliance upon the ‘loss of support’ argument; and 4) the fact that the consequences of acting in accordance with the expressivist objection would be absurd and unreasonable. This thesis concludes that the expressivist objection is an inadequate argument against the use of prenatal testing and/or disability-selective abortion, and recommends excellent, comprehensive genetic counselling as a possible compromise that respects both the rights of people with disabilities and women’s rights.
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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 | 0.034 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.048 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.020 | 0.023 |
| Insufficient payload (model declined to judge) | 0.003 | 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".