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Record W2997569873 · doi:10.25011/cim.v42i4.33110

The Vision Health Research Network and its commitment to the scholarly development of its trainees

2019· review· en· W2997569873 on OpenAlexaffvenueabout
Sonia Anchouche, Jiaru Liu, Sara Vucetic, Kim Santerre, Tianwei Ellen Zhou

Bibliographic record

VenueClinical and investigative medicine · 2019
Typereview
Languageen
FieldMedicine
TopicRetinal Diseases and Treatments
Canadian institutionsUniversité LavalUniversité de MontréalMcGill University
Fundersnot available
KeywordsVisibilityPublic relationsMedical educationProfessional developmentPolitical sciencePsychologyMedicineGeography

Abstract

fetched live from OpenAlex

The Vision Health Research Network (VHRN) is a provincial scientific organization that aims to improve the ocular health of patients across Quebec by supporting local research endeavors in vision health. The VHRN Student Committee, composed of 288 trainees with diverse backgrounds, has demonstrated its commitment to the scholarly development of its members by providing leadership opportunities, creating networking events, increasing visibility of researchers-in-training and encouraging professional advancement through educational workshops and funding programs. In this article, we review the contributions of the VHRN Student Committee and discuss its future projects.

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)0.007
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.993
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.004
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.002

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 head0.672
GPT teacher head0.560
Teacher spread0.111 · 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
DomainIncentives
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

Citations1
Published2019
Admission routes3
Has abstractyes

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