AI learning landscapes as metaphors: Expanding horizons of academic reflection through collaborative creative inquiry linked to the UNESCO draft Dubai Declaration on OER
Bibliographic record
Abstract
This paper uses UNESCO’s draft Dubai Declaration on OER (Open Educational Resources) as a focal point for critical and creative reflection. The authors examine the text to consider what stands out, the ideas and dilemmas it provokes, and how these can be expressed through a combination of written and visual language, using metaphors as a form of creative inquiry. This approach enables richer and more diverse interpretations, revealing insights that might otherwise remain hidden. A reflective narrative in the form of dialogue – interwoven with visual elements – creates space for a plurality of authentic voices among the co-authors and co-researchers. The study identifies three key themes – Transparency, Translation, and Teamwork – offering a deeper understanding of OERs, Artificial Intelligence (AI), and their implications for education. By integrating visuals and metaphors throughout, the paper bridges abstract concepts with tangible interpretations, fostering a richer and more inclusive exploration of the subject matter.
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 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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.010 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".