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Record W1482793090 · doi:10.29173/alr1344

Hard Choices and Soft Law: Ethical Codes, Policy Guidelines and the Role of the Courts in Regulating Government

2003· article· en· W1482793090 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueAlberta Law Review · 2003
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicRegulation and Compliance Studies
Canadian institutionsUniversity of SaskatchewanYork University
Fundersnot available
KeywordsSoft lawHard lawScrutinyAccountabilityLawBureaucracyPolitical scienceLaw and economicsPoliticsSociologyInternational law

Abstract

fetched live from OpenAlex

The authors examine a number of examples of "soft law": written and unwritten instruments and influences which shape administrative decision- making. Rather than rendering bureaucratic processes more transparent and cohesive, or fostering greater accountability and consistency among decision-makers, "soft law" in this context frequently reinforces artificial divisions. Moreover, it insulates decisions and decision-makers from the kinds of critical inquiry typically associated with "hard law." If it is to realize its potential as a bridge between law and policy, and lend meaning to core principles — like fairness and reliability — soft law ought to be subjected to similarly critical consideration. The authors maintain that doing so allows one to preserve soft law's promise of flexibility. Moreover, one avoids falling prey to the misleading dichotomies soft law tends to bolster in the absence of critical administrative, political, and judicial scrutiny.

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.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.969
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.028
GPT teacher head0.283
Teacher spread0.255 · 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