Protecting Conscientious Choices in National Legislation and EU Non-Discrimination Law: The Case Study of Ireland
Bibliographic record
Abstract
Abstract This paper seeks to unpack the EU non-discrimination law implications on the exercise of the right to conscientious objection in healthcare, taking Ireland’s approach to conscientious objection in termination of pregnancy services as a case study. It will be argued that protection from discrimination on the grounds of religion or belief, as guaranteed by the Framework Directive 2000/78/EC, may become applicable in the context of employment of healthcare professionals. Thus, EU non-discrimination law, as implemented by the Member States, offers a degree of additional protection for employees who otherwise may or may not have a statutory right to conscientious objection. While a detailed analysis of the Framework Directive and the CJEU caselaw shows that the protection from discrimination in the case of conscientious objection in healthcare may be rather illusory, one area where non-discrimination law might considerably broaden the scope of protection are employers with a religious ethos.
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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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.019 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.007 | 0.007 |
| 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".