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Free Access to Law and Open Source Software

2007· book-chapter· en· W2481625748 on OpenAlexaff
Daniel Poulin, Andrew Mowbray

Bibliographic record

VenueIGI Global eBooks · 2007
Typebook-chapter
Languageen
FieldSocial Sciences
TopicArtificial Intelligence in Law
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsLegislationPolitical scienceDutyLawPublic lawWork (physics)Public accessLegal researchEngineeringPublic administration

Abstract

fetched live from OpenAlex

Law consists of legislation, judicial decisions, and interpretative material. Public legal information means legal information produced by public bodies that have a duty to produce law and make it public. Such information includes the law itself (so-called primary materials) as well as various secondary (interpretative) public sources such as reports on preparatory work and law reform and resulting from boards of inquiry and available scholarly writing. The free access to law movement is a set of international projects that share a common vision to promote and facilitate open access to public legal information. The objectives of this chapter are to outline the free access to law movement, to set out the philosophies and principles behind this, and to discuss the role that open source software has played both in terms of its use and development.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesOpen science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.999
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.005
Science and technology studies0.0020.010
Scholarly communication0.0080.012
Open science0.0010.005
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0190.005

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 head0.109
GPT teacher head0.389
Teacher spread0.280 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations3
Published2007
Admission routes1
Has abstractyes

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