MétaCan
Menu
Back to cohort

Course Journals: Leveraging Library Publishing to Engage Students at the Intersection of Open Pedagogy, Scholarly Communications, and Information Literacy

2019· article· en· W2987577343 on OpenAlexaffvenue
Kate Shuttleworth, Kevin Stranack, Alison Moore

Bibliographic record

VenuePartnership The Canadian Journal of Library and Information Practice and Research · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicWikis in Education and Collaboration
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsPublishingInformation literacyElectronic publishingScholarly communicationWorkflowWork (physics)Computer scienceLiteracyIntersection (aeronautics)Library scienceMathematics educationWorld Wide WebSociologyPedagogyPsychologyPolitical scienceEngineeringThe Internet

Abstract

fetched live from OpenAlex

This article presents a case study for developing course journals, an approach to student writing and publishing that involves students in the production of an online, open access journal within a structured classroom environment. Simon Fraser University (SFU) Library’s Digital Publishing program has partnered with instructors in four different departments across the university to implement course journals in their classrooms using Open Journal Systems. Two models of course journals have emerged, both of which offer valuable learning opportunities for students around scholarly communications, information literacy, and open pedagogy. In Model 1, students act as both authors who write and submit their work for publication in the course journal and as reviewers who referee each other’s submitted work. In Model 2, students act as the course journal editors, crafting the course journal’s call for papers, soliciting content, recruiting reviewers, and managing the editorial workflow from submission to publication. This article discusses challenges and opportunities of both models as well as strategies for smooth implementation and collaboration with classroom instructors.

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)0.010
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesScholarly communication
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.732
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0200.246
Open science0.0010.000
Research integrity0.0000.001
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 head0.120
GPT teacher head0.481
Teacher spread0.360 · 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 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

Citations4
Published2019
Admission routes2
Has abstractyes

Explore more

Same venuePartnership The Canadian Journal of Library and Information Practice and ResearchSame topicWikis in Education and CollaborationFrench-language works237,207