Bibliographic record
Abstract
Over the last twenty years, I have been working in three culturally, racially, and religiously diverse countries: India, Thailand, and Malaysia. While working in these diverse countries was an enriching experience in itself, I was also fortunate to work at places that were not only diverse within themselves but also provided me the opportunity to work with hundreds of people from around the world. One of my workplaces was a K-12 international school in Thailand that followed curriculum grounded in Western philosophy where teaching and learning practices were more student-centered as compared with most traditional schools in Asia that are predominately teacher-centered. As a result of the student-centred approach, which facilitated constructive learning and amplified student agency, I began to value students' voices. Thus, when I first came across the idea of Students as Partners (SaP) five years ago while conducting a literature review for my scholarship of teaching and learning (SoTL) project, I could instantly relate to it and decided to adopt it. Since then, I have been engaged with a number of SaP collaborations (e.g., Kaur, Awang-Hashim, & Kaur, 2019; Kaur, Noman, & Nordin, 2017), and I derive immense satisfaction from its outcomes.
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 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 | 0.015 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.013 |
| Scholarly communication | 0.025 | 0.018 |
| Open science | 0.003 | 0.030 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 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".