The Scholarship of Teaching and Learning: A Scoping Review Protocol
Bibliographic record
Abstract
Scoping reviews offer a rigorous and systematic approach to examining the range and nature of literature in a particular field, identifying the existing literature and highlighting gaps where further exploration is required (Arksey & 0'Malley, 2005; Levac, Colquhoun, & O'Brien, 2010). In this article, we share our scoping review protocol—the explicit, step-by-step description of the plan for conducting the review, published separately and before completing the review. This genre of public documentation of our process is essential in scoping reviews to support careful planning and documentation, enable others to compare the protocol and completed review, support others in evaluating and/or replicating its methods, and encourage collaboration and development of follow-up research on the field (Shamseer et al, 2015).
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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 | 0.274 | 0.257 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.019 | 0.016 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.057 | 0.025 |
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".