Promoting Social Justice and Inclusion: Reflecting on the Identities of Teacher Leaders to Drive Effective Teaching and Leadership Strategies
Bibliographic record
Abstract
This research examines how social justice teacher leaders can support diverse students and colleagues in Ontario schools. It also explores how social identity and social location can influence the practices of social justice teacher leaders. Drawing on the conceptual framework that depicts the interconnectedness of teacher leaders’ perceptions, social identity, social location, critical consciousness, and praxis, this study employs a qualitative research design, wherein I conduct semi-structured interviews with ten experienced social justice teacher leaders. The majority of these leaders belonged to minoritized groups in Southern Ontario schools and hold informal leadership positions. Using the constant comparative method, I identified codes and themes grounded in the literature on social justice and teacher leadership. Results revealed how participants were committed to creating inclusion in their classrooms and schools. They described a variety of effective and inclusive teaching and social justice strategies such as empowering student voices, engaging in critical conversations to examine power and privilege, and guiding students in exploring and understanding their social locations and identities. Participants identified several strategies to develop themselves as leaders, raise critical consciousness of colleagues through collaborative activities; and advocate for students and colleagues at the school. Working conditions also influenced their leadership and social justice work. Their leadership practices were profoundly shaped by their individual identities and social locations, whether minoritized or privileged, to inform their social justice-centered practices. This study expands the scope of teacher leadership studies by highlighting the crucial social justice work that social justice teacher leaders do within their classrooms and schools.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.020 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| 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".