Literacy Text Selections in Secondary School Classrooms: Exploring the Practices of English Teachers as Agents of Change
Bibliographic record
Abstract
The purpose of this paper is to examine how Ontario secondary school English teachers make choices about which literature to teach in their courses. This will be done in order to more deeply understand why many secondary school teachers may or may not encourage students to read contemporary, social issue texts. This paper uses a critical sociology of schooling theoretical perspective to critique the study's findings. We examine the relation between policies and practice, the issue of resources and structural barriers, and how decisions are made around literary text choices. Some themes that emerged out of the interviews focus on a range of views expressed about personal agency, literary canons, gender, sexual orientation, and racism as central issues that shape text selection. We conclude by arguing for the need for policy to support individual teachers to take risks in their professional ability to select and teach contemporary social issues texts to high school students in all disciplines.
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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 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.016 | 0.019 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".