Open enough? Eight factors to consider when transitioning from closed to open resources and courses: A conceptual framework
Bibliographic record
Abstract
Transitioning from closed courses and educational resources to open educational resources (OER) and open courseware (OCW) requires considerations of many factors beyond simply the use of an open licence. This paper examines the pedagogical choices and trade-offs involved in creating OER and OCW. Eight factors are identified that influence openness (open licensing, accessibility and usability standards, language, cultural considerations, support costs, digital distribution, and file formats). These factors are examined under closed, mixed and most open scenarios to relatively compare the amount of effort, willingness, skill and knowledge required. The paper concludes by suggesting that maximizing openness is not practical and argues that open educators should strive for ‘open enough’ rather than maximal openness.
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.034 |
| Scholarly communication | 0.019 | 0.026 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".