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Record W3128327914 · doi:10.1162/dint_a_00086

Implementation of an Open Science Instruction Program for Undergraduates

2021· article· en· W3128327914 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueData Intelligence · 2021
Typearticle
Languageen
FieldDecision Sciences
TopicScientific Computing and Data Management
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsTransparency (behavior)CurriculumContext (archaeology)Best practicePolitical scienceUndergraduate researchSubject (documents)Medical educationPublic relationsEngineering ethicsPedagogyEngineeringLibrary scienceSociologyComputer scienceMedicineGeography

Abstract

fetched live from OpenAlex

The scientific, social, and economic advantages that accrue from Open Science (OS) practices—ways of doing research that emphasize reproducibility, transparency, and accessibility at all stages of the research cycle—are now widely recognized in nations around the world and by international bodies such as the United Nations and the Organization for Economic Cooperation and Development. However, program wide or coordinated instruction of undergraduate students in OS practices remains uncommon. At the University of British Columbia in Canada, we have started to develop a comprehensive undergraduate OS program that can be adapted to and woven into diverse subject curricula. We report on the context and planning of the pilot module of the program, “Open Science 101”, its implementation in first-year Biology in Fall 2019, and qualitative results of an attitudinal survey of students following their course.

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.

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)0.008
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication, Open science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.981
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0020.003
Open science0.0080.005
Research integrity0.0000.000
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 head0.430
GPT teacher head0.567
Teacher spread0.137 · 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