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Record W4308589887 · doi:10.1017/cjlj.2022.26

Subsidiarity and the Allocation of Governmental Powers

2022· article· en· W4308589887 on OpenAlexfundno aff
Michael Da Silva

Bibliographic record

VenueCanadian Journal of Law & Jurisprudence · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Philosophy and Ethics
Canadian institutionsnot available
FundersUniversity of TorontoUniversity of SouthamptonMcGill University
KeywordsSubsidiarityAllocative efficiencyLaw and economicsPolitical scienceLawBusinessEconomicsMicroeconomicsEuropean union

Abstract

fetched live from OpenAlex

Abstract Every country must allocate final decision-making authority over different issues/subjects within its boundaries. Historically, many scholars working on this topic implicitly assumed that identifying the features providing entities with justified claims for authority and the entities possessing those features would also identify which groups should have which powers (or vice versa). However, many candidate allocative principles select multiple entities as candidates for some sub-state authority and yet fail to explain which powers each should possess. Further work must explain which groups should possess which powers when and what to do when two groups can make equally-valid authority claims using the same principle. Subsidiarity, the principle under which authority should presumptively belong to the entity representing those ‘most affected’ by its exercise and capable of addressing underlying problems, is one of the few principles focused on identifying which groups should have which powers. Unfortunately, subsidiarity alone does not provide guidance on many issues/subjects. Useful subsidiarity-related guidance relies on balancing underlying justificatory interests, which do the real allocative work. Another allocative principle remains necessary. A deflationary account of subsidiarity’s allocative potential nonetheless provides insights into how to articulate a new principle and accounts of subsidiarity that can fulfill other moral roles.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.352
Threshold uncertainty score0.898

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.026
GPT teacher head0.292
Teacher spread0.266 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations14
Published2022
Admission routes1
Has abstractyes

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