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Record W2142191510

Remedies Before Administrative Tribunals: What Can Be Learned from the Common Law?

2010· article· en· W2142191510 on OpenAlexaff
Jeff Berryman

Bibliographic record

VenueSSRN Electronic Journal · 2010
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicLegal and Constitutional Studies
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsTribunalRemedial educationLegislationAnalogyAdministrative lawLawCommon lawPolitical scienceLaw and economicsBusinessEconomics
DOInot available

Abstract

fetched live from OpenAlex

The remedies that administrative tribunals can administer is an under studied area. Often, empowering legislation simply extoils an administrative tribunal to do what is ''fair and just''. In this paper, I argue that when confronted with open-textured remedial provisions, tribunals may often benefit from drawing by analogy from developed common law principles used to quantify monetary and non-monetary relief.

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)0.125
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.036
Threshold uncertainty score0.189

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0360.125
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.002
Science and technology studies0.0090.045
Scholarly communication0.0210.039
Open science0.0070.009
Research integrity0.0230.021
Insufficient payload (model declined to judge)0.0140.002

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 head0.046
GPT teacher head0.257
Teacher spread0.212 · 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 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

Citations0
Published2010
Admission routes1
Has abstractyes

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Same venueSSRN Electronic JournalSame topicLegal and Constitutional StudiesFrench-language works237,207