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Record W3176229594 · doi:10.1111/1468-2230.12648

Citizenship Stripping, Fair Procedures, and the Separation of Powers: A Critical Comment on <i>Damache</i> v <i>Minister for Justice</i>

2021· article· en· W3176229594 on OpenAlexaff
Conor Casey

Bibliographic record

VenueModern Law Review · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicLegal principles and applications
Canadian institutionsCasey House
Fundersnot available
KeywordsLawSupreme courtEconomic JusticePolitical scienceSeparation of powersCitizenshipIrishJudgementConstitutionSociologyPhilosophyPolitics

Abstract

fetched live from OpenAlex

Abstract Damache v Minister for Justice concerned a constitutional challenge to section 19 of the Irish Nationality and Citizenship Act 1956. This section outlined the statutory process the executive branch, acting through the Minister for Justice, had to follow before revoking a certificate of naturalisation. The appellant successfully argued this process was an unconstitutional breach of fair procedures. The judgement will be of interest both to Irish and other public lawyers for its treatment of fair procedures, which the Supreme Court approached in a regrettably blinkered way – seeing only one constitutional principle when several others were at stake. The judgment is a stark reminder for both Irish and comparative lawyers of the fact that the concrete demands of fair procedures must be balanced with a range of competing institutional goods and principles equally important to constitutional democracies: from administrative efficiency to structural principles stemming from the separation of powers.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.036
metaresearch head score (Gemma)0.043
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.246
Threshold uncertainty score0.490

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0360.043
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.002
Science and technology studies0.0150.039
Scholarly communication0.0150.009
Open science0.0070.004
Research integrity0.0510.053
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.065
GPT teacher head0.401
Teacher spread0.336 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2021
Admission routes1
Has abstractyes

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