Toward a Critical Approach for OER: A Case Study in Removing the ‘Big Five’ from OER Creation
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.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.021 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".