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Record W7009932786

2022 Gladys Nipp and Stephen Mah Award-Winning Research Proposal

2022· article· en· W7009932786 on OpenAlexaboutno aff

Bibliographic record

VenueUVic’s Research and Learning Repository (University of Victoria) · 2022
Typearticle
Languageen
FieldComputer Science
TopicLibrary Collection Development and Digital Resources
Canadian institutionsnot available
Fundersnot available
KeywordsResearch proposalResearch methodologyHuman researchExperimental researchPostgraduate research
DOInot available

Abstract

fetched live from OpenAlex

In this award-winning 500 word research proposal, PhD student Xue Ma describes a proposed use of UVic Libraries' Chinese-Canadian collections.

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)0.010
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.130
Threshold uncertainty score0.433

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0070.002
Scholarly communication0.0100.003
Open science0.0020.005
Research integrity0.0060.006
Insufficient payload (model declined to judge)0.1300.095

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 head0.031
GPT teacher head0.259
Teacher spread0.228 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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
Published2022
Admission routes1
Has abstractyes

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