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Record W4289783348 · doi:10.26685/urncst.398

The BOLD Science Conference 2022: Abstract Book Featuring the Work of Undergraduate Science College Students

2022· article· en· W4289783348 on OpenAlexafffund
June Kim

Bibliographic record

VenueUndergraduate Research in Natural and Clinical Science and Technology (URNCST) Journal · 2022
Typearticle
Languageen
FieldDecision Sciences
TopicInterdisciplinary Research and Collaboration
Canadian institutionsConcordia University
FundersConcordia University
KeywordsMultidisciplinary approachTheme (computing)Scientific literacyScience educationMinor (academic)Work (physics)PsychologyMathematics educationMedical educationEngineering ethicsSociologyEngineeringComputer sciencePolitical scienceMedicineSocial science

Abstract

fetched live from OpenAlex

The BOLD Science Conference is organized by the Science College and Science College Student Association at Concordia University and was held on May 6th and 7th. This year’s theme was wildfires. The conference promotes the multidisciplinary nature of science and science communication/literacy. It highlights the exceptional research performed by the undergraduate students at the Science College, which were presented as virtual poster presentations through the Gathertown online platform. The Minor in Multidisciplinary Studies in Science (Science College) at Concordia is a program for undergraduates interested in pursuing a career in scientific research. The students are required to complete a minimum of two research projects outside of their Major, in the spirit of promoting the multidisciplinary nature of research.

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)0.103
metaresearch head score (Gemma)0.015
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.360
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1030.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.031
Science and technology studies0.0160.041
Scholarly communication0.0020.002
Open science0.0090.008
Research integrity0.0000.005
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 head0.114
GPT teacher head0.496
Teacher spread0.382 · 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

Citations0
Published2022
Admission routes2
Has abstractyes

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