Social Justice in Preservice and Graduate Education: A Reflective Narrative Analysis
Bibliographic record
Abstract
This research shows how two teacher educators, one from Canada and one from the United States, have attempted to imbue their preservice and graduate education practices with a sense of social justice, despite the downgrading of the importance of social justice by accreditation agencies such as National Council for the Accreditation of Teacher Education (NCATE) in the United States. Most specifically, narrative exemplars (Lyons & LaBoskey, 2002 Lyons, N. and LaBoskey, V. 2002. Narrative inquiry in practice: Advancing the knowledge of teaching, New York, NY: Teachers College Press. [Google Scholar]) are presented and reflectively analyzed through Connell's (1993) three principles of curricular justice, which lead toward or away from social justice in preservice and graduate education. As a consequence of this inquiry into lived educational practice, rich insights into teaching, learning, and human interaction emerge.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".