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Record W4246966547 · doi:10.18357/tar32201211637

Editor's Introduction

2012· article· en· W4246966547 on OpenAlexvenueaboutno aff
Adam Gaudry

Bibliographic record

VenueThe Arbutus Review · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousScholarshipSituatedAcknowledgementVariety (cybernetics)SociologyReputationPolitical scienceMedia studiesEnvironmental ethicsPublic relationsSocial scienceLawEcology

Abstract

fetched live from OpenAlex

The University of Victoria, in many ways, is a special place. It is one of the few universities in Canada where Indigenous issues are taught, discussed, and debated with the attention and care they deserve—and thanks to a cadre of excellent faculty and instructors, the debate has been a respectful one. The sizeable Indigenous faculty presence on campus, as well as a variety of programming options has created a healthy space for Indigenous scholarship. Perhaps one of the most important aspects of UVic is the constant acknowledgement that UVic is situated on the lands of the Coast and Straits Salish people. The presence of local Indigenous peoples—students, faculty, staff, and community members—as well as Indigenous peoples from further afield, makes for an enriching intellectual and social environment for those of us who study Indigenous issues here. In this atmosphere, learning extends to places outside of the classroom and provides for dynamic relationships with new people from different places with different perspectives. The University of Victoria has, quite deservedly, also developed a reputation as a world leader in Indigenous Studies, something that I have been reminded of at the many conferences I have attended across the continent. It is well known for producing some groundbreaking scholarship and attracting world-class students.

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 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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.640
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.019
GPT teacher head0.336
Teacher spread0.317 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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
Published2012
Admission routes2
Has abstractyes

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