Toward a Critical Approach for OER: A Case Study in Removing the ‘Big Five’ from OER Creation
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.
Bibliographic record
Abstract
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.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it