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Record W7161229912 · doi:10.7202/1125044ar

Law School Success: A Matter of Heart

2024· article· fr· W7161229912 on OpenAlexaboutno aff
Yan Campagnolo

Bibliographic record

VenueOttawa Law Review · 2024
Typearticle
Languagefr
FieldComputer Science
TopicArtificial Intelligence Applications
Canadian institutionsnot available
Fundersnot available
KeywordsAcronymKey (lock)Word (group theory)Subject matterFree speech

Abstract

fetched live from OpenAlex

On September 3, 2024, at the start of the academic year, Vice-Dean Yan Campagnolo addressed the new students of the University of Ottawa Faculty of Law. His speech outlined the key qualities that the most successful students exhibit: they are consistent, organized, efficient, ultra-hardworking, and rigorous. Together, the first letters of each of these qualities make the acronym “C.O.E.U.R.”, the French word for “heart”. The speech shows that success at law school is thus ultimately an affaire de cœur—a matter of heart .

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)0.022
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: none
Teacher disagreement score0.389
Threshold uncertainty score0.774

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0090.012
Scholarly communication0.0130.005
Open science0.0010.004
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0090.003

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 head0.049
GPT teacher head0.349
Teacher spread0.300 · 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
Published2024
Admission routes1
Has abstractyes

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