Call and Response: Inquiry-Based Learning as a Critical Pedagogy in the Scholarship of Teaching and Learning to Promote Transformation and Transformational Leadership
Bibliographic record
Abstract
We have been called to action as teachers—to become leaders of change in society and move forward in good ways in the Scholarship of Teaching and Learning (SoTL). To move forward in good ways, we must identify and work to deconstruct systemic racism and white supremacy embedded in all colonial institutions, including institutions of higher education. We can start this journey in higher education by responding to the call to engage with new ways of knowing and doing; we can apply critical pedagogies in the classroom. Responding to Dr. Gabrielle Weasel Head’s question “What might we miss if we do not spark students’ curiosity?,” I suggest that through the application of inquiry-based learning (IBL), we might inspire students to become curious and engage with us in the goals of social justice. In this call-and-response article, I engage with the literature and reflect on the application of IBL as both student and teacher.
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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 | 0.019 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.032 |
| Scholarly communication | 0.020 | 0.017 |
| Open science | 0.007 | 0.020 |
| Research integrity | 0.044 | 0.045 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".