It's Just Good Teaching: Creating Inclusive Elementary Classrooms Through Feminist Pedagogy
Bibliographic record
Abstract
As Ontario schools become more and more diverse, inclusion in the classroom is becoming an ever more important issue for educators (Ontario Ministry of Education, 2009). This paper examines how feminist pedagogy is used in elementary classrooms to contribute to inclusive learning environments. With a lack of current studies on the topic and a lack of educator’s own voices on the topic, feminist pedagogy in the elementary classroom has been relatively under researched within Ontario (Woodham Digiovanni & Liston, 2015). Using qualitative research approaches, three semi-structured interviews were conducted with educators who identified with using aspects of feminist pedagogy in their practices. From the interviews, the participants identified key aspects of feminist pedagogy, including acknowledging gender difference, acknowledging the diversity of students and focusing on critical thinking in the classroom. The study found that student empowerment and student autonomy are key factors of feminist pedagogy that contribute to inclusion in the elementary classroom. From these findings, a major implication on the broad educational community suggests using strategies focusing on student empowerment and autonomy are effective ways to promote inclusion in elementary classrooms.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".