Autonomy and prenuptial agreements in Ireland: a relational analysis
Bibliographic record
Abstract
Abstract Unlike England and Wales, Ireland has not yet moved from the traditional common law rejection of prenuptial agreements. Nevertheless, similar policy concerns continue to be debated in both jurisdictions, particularly regarding the balance between autonomy and fairness concerns, and gender equity. In 2007, an Irish ministerial Study Group recommended limited recognition of prenuptial agreements, foreshadowing similar proposals by the Law Commission for England and Wales in 2014. However, the Irish recommendations were never implemented, despite sustained lobbying. This paper draws on relational theory to scrutinise the Study Group's proposals, identifying its core assumptions and their implications. The paper contends that Irish courts dealing with spousal agreements have tacitly accepted liberal conceptualisations of autonomy, which may lead to injustice. Furthermore, the Study Group's recommendations have been overtaken by events. Recent decisions on spousal agreements emphasise respect for party autonomy, without interrogating what this means. This could be problematic if applied to prenuptial agreements. Accordingly, the paper suggests modifications to the Study Group's proposals, to address relational concerns. In this regard, the paper speaks to the broader debate on family autonomy, and draws on comparative perspectives, including the recommendations of the Law Commission for England and Wales, and the Canadian experience.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".