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
Problem-Based Learning (PBL), a student-centred learning approach that focuses on reallife problems in higher education, has been around for more than fifty years (Servant-Miklos, Schmidt & Norman, 2019).It originated in 1969 at McMaster University's medical school in Canada and spread to other academic disciplines including engineering (Guerra et al., 2017), law (Cleassens, 2020), humanities (Kloeg, 2023), and psychology (Wiggins et al., 2016), becoming a well-recognized approach in universities worldwide.This wide-ranging diversity of applications has yielded, on the one hand, a rich body of theory and practice, with different PBL models emerging to meet diverging curricular requirements and learning objectives (Savin-Baden, 2003).On the other hand, it has also created some confusion, wherein the differences in philosophical understanding, didactic basis, and concrete practice between the academic disciplines have not been discussed thoroughly.At the same time, PBL is facing a host of new challenges from emerging global threats and opportunities, such as climate change, biodiversity loss, socioeconomic inequality, and technological progress, including artificial intelligence, with a commensurate rise in ethical challenges.Faced with the rapidly evolving environmental emergency, some PBL scholars have recently called for PBL to "change or risk irrelevance" (Servant-Miklos, Dolmans & Ryberg, 2023), advocating for the development of more socially engaged, transdisciplinary, and sustainable approaches to PBL.
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.
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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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