Methods of deference in human rights adjudication
Bibliographic record
Abstract
The phenomenon of judicial deference to the executive or legislature in human rights adjudication has elicited extensive scholarly discussions. Whilst much has been written on whether and when courts should defer, this thesis is the first to systematically probe two questions regarding how courts should defer. The first is what devices courts should use to express the various reasons for deference. I explain that in jurisdictions that satisfy certain background conditions (which include the jurisdictions whose case law this thesis draws upon, namely, Canada, Hong Kong, Ireland, Israel, New Zealand and United Kingdom), courts have three sets of grounds for deference: grounds that relate to arriving at correct outcomes on the rights issue in question, to respect for constitutional legitimacy, and to the achievement of other aspects of the common good that courts should take into account in adjudication. Noting that courts have at their disposal six devices for exercising deference – the burden of proof, standard of proof, standard of review, giving of weight to views, choice of interpretation and choice of remedy – I argue that sometimes specific devices must be used because other devices are unable to express, or express to the appropriate degree, the reasons for deference in a particular instance. The second question that this thesis examines is how the methods of determining when and how to defer can be made more practicable for judges and litigants without undue compromise of those methods’ ability to fulfil the reasons for deference in a particular case. I propose four techniques for striking a balance between these two considerations: the use of rules, presumptions and factorial analysis; mapping certain normative considerations for deference onto specific devices; developing clear and reliable indicators of deference; and developing finite scales for various devices and levels of scrutiny that combine devices.
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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 | 0.131 | 0.287 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.005 | 0.025 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.007 | 0.016 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".