SoTL Inquiry in Broader Curricular and Institutional Contexts: Theoretical Underpinnings and Emerging Trends
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
Universities around the world increasingly recognize the importance of offering high quality, high-engagement student learning experiences in their undergraduate and graduate programs. While the SoTL movement and literature has gained considerable recognition and momentum over the past two decades, much less inquiry has focused on institutional and program-level educational reforms. This paper calls for a more expansive view and strategic use of SoTL inquiry in order to make substantive contributions to curriculum renewal, educational leadership practices, and, most importantly, the quality of undergraduate and graduate degree programs. Theoretical underpinnings, emerging trends, challenges, and strategic supports to enhance the effectiveness and efficiency of curricula within and across diverse disciplinary contexts are discussed.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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