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Record W3016234850 · doi:10.5944/openpraxis.11.4.1020

Toward a Critical Approach for OER: A Case Study in Removing the ‘Big Five’ from OER Creation

2019· article· en· W3016234850 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.

Bibliographic record

VenueOpen Praxis · 2019
Typearticle
Languageen
FieldComputer Science
TopicOpen Education and E-Learning
Canadian institutionsUniversity of AlbertaYork University
Fundersnot available
KeywordsComputer scienceOpen educational resourcesSoftwareProcess (computing)World Wide WebSoftware engineeringData scienceProgramming language

Abstract

fetched live from OpenAlex

This paper examines the role of proprietary software in the production of open educational resources (OER). Using a single case study, the paper explores the implications of removing proprietary software from an OER project, with the aim of examining how complicated such a process is and whether removing such software meaningfully advances a critical approach to OER. The analysis reveals that software from the Big Five technology companies (Apple, Alphabet/Google, Amazon, Facebook and Microsoft) are deeply embedded in OER production and distribution, and that complete elimination of software or services from these companies is not feasible. The paper concludes by positing that simply rejecting Big Five technology introduces too many challenges to be justified on a pragmatic basis; however, it encourages OER creators to remain critical in their use of technology and continue to try to advance a critical approach to OER.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.461
Threshold uncertainty score0.971

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.069
GPT teacher head0.356
Teacher spread0.288 · 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