Bibliographic record
Abstract
This qualitative research study explores how teachers who write social justicefocused curriculum support resources conceptualize curriculum and social justice. Curriculum used in schools reflects underlying assumptions and choices about what knowledge is valuable. Class-based, cultural, racial, and religious stereotypes are reinforced in schooling contexts. Are the resources teachers create, select, and use to promote social justice reproducing and reinforcing forms of oppression? Why do teachers pursue social justice through curriculum writing? What are their hopes for this work? Exploring how Teachers' beliefs and values influence cy.rriculum writing engages the teachers writing and using curriculum support resources in critical reflective thought about their experiences and efforts to promote social justice. Individual and focus group interviews were conducted with four teacher-curriculum writers from Ontario schools. In theorizing my experiences as a teacher-curriculum writer, I reversed roles and participated in individual interviews. I employed a critical feminist lens to analyze the qualitati ve data. The participants' identities influenced how they understand social justice and write curriculum. Their understandings of injustices, either personal or gathered through students, family members, or oth.e. r teachers, influenced their curriculum writing . The teacher-curriculum writers in the study believed all teachers need critical understandings of curriculum and social justice. The participants made a case for representation from historically disadvantaged and underrepresented groups on curriculum writing teams. In an optimistic conclusion, the possibility of a considerate curriculum is proposed as a way to engage the public in working with teachers 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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.026 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.006 |
| 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".