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Record W4225123774 · doi:10.26685/urncst.375

UBC 2022 Multidisciplinary Undergraduate Research Conference: Inspire Change

2022· article· en· W4225123774 on OpenAlexaffabout
Rafid Haq, Sarah Jiang, Adrian Chen, Samantha N. Cortez, Nathan Louie, Aimee Koristka, Celina Chan, Dayle Balmes, Nadya Tan, Sylvia Wang, Leticia P. Garcia, Selynn Yeap

Bibliographic record

VenueUndergraduate Research in Natural and Clinical Science and Technology (URNCST) Journal · 2022
Typearticle
Languageen
FieldDecision Sciences
TopicInterdisciplinary Research and Collaboration
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPresentation (obstetrics)Multidisciplinary approachTheme (computing)OutreachMedical educationLibrary sciencePsychologyComputer scienceSociologyMedicineWorld Wide WebPolitical science

Abstract

fetched live from OpenAlex

The Multidisciplinary Undergraduate Research Conference (MURC) is a conference for undergraduate students at the University of British Columbia (UBC) to showcase their research in front of their fellow UBC students, family, and friends. Researchers may choose one of three formats to showcase their research: oral presentation, poster, or virtual presentation. Presentations are categorized into four multidisciplinary themes: Health and Wellness; Individual, Community and Society; Sustainability and Conservation; and Innovation and Technology. Presentations were evaluated by UBC graduate students and faculty to provide students with feedback on their projects and presentation skills. MURC 2022 was the 19th iteration of the conference and the first ever hybrid (both in-person and virtual) undergraduate research conference held at UBC. The theme for MURC 2022 was Inspire Change. This theme seeks to approach research in a manner that inspires new generations to see that change as an agent of positivity and growth. The in-person components of the conference were held at the UBC Vancouver campus, with poster sessions in the Marine Drive Ballroom and oral presentations in the various lecture rooms in the West Mall Swing Space Building. Virtual presentations were held using the Zoom Meetings videoconferencing software.

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)0.088
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics, Science and technology studies, Scholarly communication, Open science, Research integrity
Consensus categoriesMetaresearch, Science and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.725
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0880.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0060.022
Science and technology studies0.0100.011
Scholarly communication0.0020.002
Open science0.0040.009
Research integrity0.0000.011
Insufficient payload (model declined to judge)0.0000.000

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 head0.265
GPT teacher head0.534
Teacher spread0.269 · 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; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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
Published2022
Admission routes2
Has abstractyes

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