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Record W2980439298 · doi:10.19173/irrodl.v20i4.4215

Open Universities and Open Educational Practices

2019· article· en· W2980439298 on OpenAlexaffvenue
Irwin DeVries

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2019
Typearticle
Languageen
FieldComputer Science
TopicOpen Education and E-Learning
Canadian institutionsThompson Rivers University
Fundersnot available
KeywordsOpen educational resourcesOpen educationOpen learningDistance educationHigher educationEducational technologySituatedOpen universityPedagogySociologyKnowledge managementPublic relationsPolitical scienceComputer scienceTeaching methodCooperative learning

Abstract

fetched live from OpenAlex

The purpose of this study is to provide an overview of how open universities depict their current institutional engagement in open educational practices. In view of the growth of programming for non-traditional students by conventional universities, particularly through online learning and increasing interest in open educational practices, the intention is to gain a better understanding of the unique contributions currently made, or potentially to be made, by open universities in comparison to conventional universities. The study is conducted through a content analysis of open university websites, exploring key themes related to access-oriented open educational practices derived from terms and related concepts in relevant literature. With the growth of distance education, online learning, and other emerging access-oriented open educational practices in traditional higher education, open universities should be uniquely situated to provide visible leadership in these domains. The open university website content analysis explores the extent to which this is the case.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesOpen science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.999
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0050.022
Scholarly communication0.0150.017
Open science0.0010.013
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0070.001

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 head0.109
GPT teacher head0.484
Teacher spread0.375 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations13
Published2019
Admission routes2
Has abstractyes

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