Exploring the Impacts of Students’ Characteristics, Pedagogical Activities, and Course Structure on Personal Resonance and Practical Applications of Transformative Pedagogy
Bibliographic record
Abstract
Sociology instructors are increasingly turning to transformative pedagogy as a tool to help students recognize and respond to social injustices. This is increasingly important amid growing political and institutional backlash against diversity, equity, and inclusion (DEI) initiatives in higher education. Yet the factors that shape the effectiveness of this approach remain underexplored. This study examines a large second-year university Sociology of Families course (N = 299) grounded in the concept of critical hope. We investigate how course structure, pedagogical strategies, and student characteristics influence two key outcomes: students’ personal resonance with the material and their reported motivation to enact social change. Findings indicate that both resonance and practical application of critical hope were positively associated with the use of 14 transformative pedagogical strategies and with longer course durations. Students with accessibility needs were less likely to find the content personally meaningful, and White-identifying students were less likely to express motivation for social action. We conclude by offering recommendations for increasing the inclusivity and impact of transformative pedagogy, particularly in a climate of rising resistance to DEI-focused education.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".