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Record W2748555408

Foreword of UBC Special Issue

2016· article· en· W2748555408 on OpenAlexaboutno aff
Obiora Chinedu Okafor

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Education and Practice Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science
DOInot available

Abstract

fetched live from OpenAlex

In 1997, the first graduate law student conference ever to be held at the University of British Columbia (UBC), and perhaps in all of Canada, was convened at its Green College. It was primarily organized by the present author (now a professor at the Osgoode Hall Law School) and Jaye Ellis (currently a professor of law at McGill University. Jaye and I also had the dedicated help and support of a number of other graduate law students. Critically, both of us profited immensely from the robust support, extraordinary commitment and expert guidance of Professor W. Wesley Pue, who at the time held the Nemetz Chair in Legal History and also served as the Director of Graduate Legal Studies at UBC. Professor Pue’s highly imaginative mind, his highly developed communication skills, his wise advice, and his steadfastness were extremely helpful as my collaborator and I plotted, planned and executed on what began its eventful life in a conversation between Jaye and I while we sat in the then graduate law student’s lounge at UBC. Ever the committed mentor, Professor Pue enthusiastically threw the full and considerable weight of the graduate law program behind us – two young graduate students who were still green in the business of conference organizing. Since there was at the time no Canadian precedent for what we planned to do, no model to follow, and no manual to read out of, Professor Pue’s expertise, experience and sage advice was a critical factor in shaping the success that the event eventually was. The financial generosity shown to this first conference by the Pue-led graduate program was extremely helpful as well. It is, thus, safe to say that we could not have done it without his support. Professor Karin Mickelson, who was a key member of my doctoral supervision committee, was also a committed and able adviser, and an invaluable source of support.

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)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.583
Threshold uncertainty score0.594

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0040.001
Scholarly communication0.0080.005
Open science0.0020.002
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.5830.536

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 head0.042
GPT teacher head0.395
Teacher spread0.353 · 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
GenreEditorial

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
Published2016
Admission routes1
Has abstractyes

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