Teaching Microaggressions, Identity, and Social Justice: A Reflective, Experiential and Collaborative Pedagogical Approach
Bibliographic record
Abstract
The purpose of social work is actualized through its commitment to diversity and differences in practice, as well as human rights, social, economic, and environmental justice. A review of literature on microaggressions and oppression against marginalized and vulnerable populations suggests important themes that social work instructors need to examine with students. It is unclear to what extent instructors use pedagogical tools to gain knowledge, skills, and critical consciousness to navigate social justice contents and manage difficult conversations with diverse student groups in class settings. Not much attention is paid in social work education on how well instructors are prepared to teach this content in depth and what challenges they face when facilitating highly sensitive and difficult discussions with students. This article described and evaluated five sets of reflective, experiential, and collaborative activities in a social justice course designed to help social work students examine the histories of various identity groups that have experienced discrimination and oppression and increase their self-awareness of both privilege and personal bias in one’s life. These activities include: (1) reflective reading notes; (2) critical reflection paper; (3) brief lecture and experiential class activities and discussion; (4) collaborative group presentations and role-plays; and (5) cultural competency plan. Thirty-two students completed the evaluation surveys to assess their overall feedback about course activities and 36 students completed anonymous online course evaluations to assess their level of attainment in all course competencies. Student feedback collected in course evaluations and surveys, students’ self-assessment of attainment of course competencies, and the instructor’s critical reflection and self-assessment, suggest that teaching social justice using a reflective, experiential, and collaborative pedagogical approach has a promising potential for advancing course objectives. Through these activities, students increased their knowledge on a range of topics such as racism, oppression, microaggression, social identities, intersectionality, privilege, and cultural humility, enhanced their understanding of various forms of prejudice and discrimination, and acquired critical skills and cultural competence that have direct application in social work field.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".