Powerful stories, powerful conversations: using literature to teach for social justice
Bibliographic record
Abstract
Creating opportunities for students to cultivate the skills they need to participate justly in an increasingly complex, pluralistic world is a considerable challenge for teachers. Informed by critical pedagogy and reader response theories, this doctoral research examines how three grade 7 and 8 English language arts teachers used literature study to foster student exploration and discussion of social justice issues. Classroom observations and interviews with the three teachers were conducted at three different elementary school sites in southwestern Ontario in 2005. Two of the teachers used novel study and one used a set of thematically related poems to explore themes such as personal and social responsibility and power inequity. Given the complexity and potential risks of addressing social justice issues in the classroom, this research examined the factors that facilitated and impeded this challenging pedagogical work, and how they created opportunities for dialogic exploration of potentially conflictual issues. Data were analysed using grounded theory methodology. Findings suggested that the three teachers felt somewhat constrained by institutional (curriculum coverage pressures) and personal (concerns about reprisals) factors, but were primarily concerned with balancing their commitment to exploring social justice issues openly in their classrooms with the safety and comfort of their students. The teachers viewed literature study as a powerful vehicle for social justice education because it invited both emotional and cognitive engagement with themes and offered multiple entry points to engage diverse learners. Students at all three sites appeared most engaged with texts and topics that were relevant to their own experiences, and when given opportunities to explore multiple interpretations. However, marked differences were observed among the three classrooms in the number of students who participated in discussions, the range of perspectives included, and the level of criticality with which topics were explored. These differences stemmed from the interplay of the teachers' text and topic selections and the pedagogical approaches they used to foster student exploration and talk. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.020 | 0.028 |
| Scholarly communication | 0.015 | 0.021 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".