Conceptual framework for parametrically measuring the desirability of open educational resources using D-index
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
<p>Open educational resources (OER) are a global phenomenon that is fast gaining credibility in many academic circles as a possible solution for bridging the knowledge divide. With increased funding and advocacy from governmental and nongovernmental organisations paired with generous philanthropy, many OER repositories, which host a vast array of resources, have mushroomed over the years. As the inkling towards an open approach to education grows, many academics are contributing to these OER repositories, making them expand exponentially in volume. However, despite the volume of available OER, the uptake of the use and reuse of OER still remains slow. One of the major limitations inhibiting the wider adoption of OER is the inability of current search mechanisms to effectively locate OER that are most suitable for use and reuse within a given scenario. This is mainly due to the lack of a parametric measure that could be used by search technologies to autonomously identify desirable resources. As a possible solution to this limitation, this concept paper introduces a parametric measure of desirability of OER named the D-index, which can aid search mechanisms in better identifying resources suitable for use and reuse.</p>
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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 | 0.015 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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