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Record W3210381954 · doi:10.5281/zenodo.3776724

Scientific Culture Change from Above and Below at UBCO: Implementation of a Comprehensive Open Science Library Information Literacy Program for Undergraduates

2020· article· en· W3210381954 on OpenAlexaffabout
Sharon L. Hanna

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2020
Typearticle
Languageen
FieldComputer Science
TopicOpen Education and E-Learning
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsInformation literacyLibrary scienceInformation scienceComputer scienceSociology

Abstract

fetched live from OpenAlex

Many agree that science must change to become more open. But change is difficult, and<br> universities bound by size and conservative traditions may be slow to introduce incentives and<br> rewards for the practice of Open Science (OS) (Lancaster 2016). Johannes Vogel, director general of the Museum für Naturkunde and Professor of Biodiversity<br> and Public Science at Humboldt University in Berlin, recognizes the power that young people<br> have to mobilize movements and suggests that they can help to open up science. In his article<br> “Scientists need to learn from the young”, Vogel (2019) states, Science must learn to listen, open up and again become part of the community. Such a<br> transformation … will cost time and money, including the restructuring of the incentive<br> systems in science itself. Students have long been catalysts of social and political change, so why not introduce them to<br> OS at an early stage? The University of British Columbia’s Okanagan Campus in Kelowna, B.C.,<br> Canada recently took the bold step of funding a two-year strategic project that includes the<br> creation, deployment, and evaluation of a comprehensive Open Science library information<br> literacy (IL) program specifically for undergraduates. Project leads from the Library and the<br> Department of Biology intend to make the core tenets and practices of OS second nature to<br> future graduates and global citizens. We believe that this initiative is unique in North America: it<br> breaks new ground both as an effort from the university to foster change at the grassroots and<br> as a model for comprehensive undergraduate IL instruction in Open Science. Given the nascent nature of the Open Science (OS) movement, scant literature on best<br> practices in library OS IL instruction exists. Extant publications on this topic (e.g. Lopes et al.,<br> 2019) tend to focus on IL instruction of researchers pursuing graduate studies – that is, EU<br> Level 2 or higher. One exception is Ayris &amp; Ignat, who suggest involving undergraduates (EU<br> Level 1 students) in citizen science projects (Ayris &amp; Ignat, 2018). The beginning stages of the project are being implemented in the 2019/2020 academic year as<br> a nine-module program that will interweave Open Science principles and practice into the<br> Biology undergraduate curriculum, and five of the modules will have been delivered by the end<br> of the 2019/2020 academic year. Ultimately, the project will attempt to secure further funding to<br> instate the program as a micro-credential; expand it to other areas of study, starting with<br> Psychology and Human Kinetics; and establish undergraduate research awards conditional on<br> adherence to Open Science practices. This talk will outline the undergraduate program’s modules and documents the creation,<br> delivery, and evaluation of an introductory module for first-year Biology students on the Canvas<br> online learning platform in Fall 2019. The module, “OS 101”, will give students an overview of<br> the practical challenges of conducting reproducible research, the societal impact of<br> irreproducible research, and philosophical and ethical issues surrounding Open Science.<br> Completed modules will be made available as Open Educational Resources in markdown<br> format on GitHub.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.884
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0060.007
Open science0.0020.004
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.056
GPT teacher head0.317
Teacher spread0.261 · 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 teacher head, not a consensus.

Study designNot applicable
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
Published2020
Admission routes2
Has abstractyes

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