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Record W3200042792

LibGuides: Legal Citation: Practice exercises

2021· libguides· en· W3200042792 on OpenAlexaboutno aff
Theresa Bell

Bibliographic record

Venuenot available
Typelibguides
Languageen
FieldSocial Sciences
TopicArtificial Intelligence in Law
Canadian institutionsnot available
Fundersnot available
KeywordsCitationBusinessPolitical sciencePublic relationsLaw
DOInot available

Abstract

fetched live from OpenAlex

Learn more about how to cite legal materials using the rules of the McGill Law Journal's Canadian Guide to Uniform Legal Citation

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)0.006
metaresearch head score (Gemma)0.032
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.195
Threshold uncertainty score0.654

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.032
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0060.008
Science and technology studies0.0020.001
Scholarly communication0.0050.007
Open science0.0020.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.1950.090

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 head0.084
GPT teacher head0.400
Teacher spread0.315 · 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
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

Citations0
Published2021
Admission routes1
Has abstractyes

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