COVID-19 Vaccination and Legal Preparedness: Lessons from Ireland
Bibliographic record
Abstract
Ireland has been a leader in the COVID-19 vaccine rollout in the EU, with almost 80% of the eligible population (aged over 5 years) fully vaccinated at the time of writing. The success of the vaccine rollout in this jurisdiction notwithstanding, the legal frameworks supporting the rollout had significant lacunas. Two aspects in particular highlighted a lack of legal preparedness: the inadequacy of the legal framework for consent and the absence of a vaccine injury redress scheme. This paper explores these components of the COVID-19 vaccine rollout through the lens of legal preparedness. Whilst most often discussed in the context of command and control measures such as social distancing requirements and regional lockdowns, this paper argues for an expanded understanding of what it means to be legally prepared, highlighting the importance of the preparedness of domestic legal frameworks.
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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 | 0.020 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.026 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.006 | 0.012 |
| 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".