Bibliographic record
Abstract
Teaching for social justice is an explicit goal of most teacher education programs. However, this mandate has been criticized by scholars who claim that social justice is an undertheorized and vague concept that is often disconnected from methods courses that focus on content-specific pedagogies. This study seeks to address this disconnect by exploring how an Equity Literature Circles (ELC) framework within a literacy methods course can enhance teacher candidates’ (TCs’) understanding of the relationship between literacy instruction, diversity, and social justice. Drawing on the perceptions of ten TCs enrolled in a teacher education program in western Canada, data for the study was generated from classroom artifacts, an individual survey, and a focus group interview. Upon analysis of the data, the findings suggest that an ELC framework is an effective instructional strategy for enhancing TCs’ understanding of intersecting aspects of diversity, equity-focused literacy instruction, and teaching about and for social justice. Teaching for social justice is an explicit goal of most teacher education programs. However, this mandate has been criticized by scholars who claim that social justice is an undertheorized and vague concept that is often disconnected from methods courses that focus on content-specific pedagogies. This study seeks to address this disconnect by exploring how an Equity Literature Circles (ELC) framework within a literacy methods course can enhance teacher candidates’ (TCs’) understanding of the relationship between literacy instruction, diversity, and social justice. Drawing on the perceptions of ten TCs enrolled in a teacher education program in western Canada, data for the study was generated from classroom artifacts, an individual survey, and a focus group interview. Upon analysis of the data, the findings suggest that an ELC framework is an effective instructional strategy for enhancing TCs’ understanding of intersecting aspects of diversity, equity-focused literacy instruction, and teaching about and for social justice.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".