Bibliographic record
Abstract
We are pleased to share the Proceedings of the 51st Annual Conference of the International Association of School Librarianship conference and the 26th International Forum on Research in School Librarianship held in Rome, Italy from July 17-21, 2023. The Research Papers and Research Abstracts were peer-reviewed by a minimum of three school library researchers. A very special thank you to the conference committee chaired by Dr. Luisa Marquardt and Dr. Anna Cascade.
 To cite these proceedings follow this example.
 Ruffles, D. (2023). Transformative learning: The impact of deeper learning approaches in enhancing the transversal competencies. In C. Stang & J. L. Branch-Mueller (Eds.). Proceedings of the 51st annual conference of the International Association of School Librarianship and the 26th international forum on research in school librarianship. Edmonton, Canada: University of Alberta. (Add the direct link to your paper as well as the unique DOI)
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.006 |
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".