A Narrative Inquiry Into the Experiences of Participants in a High School Feminist English Language Arts Class
Bibliographic record
Abstract
While many scholars have engaged with the benefits of feminist pedagogy in schools, most conceptualize feminist pedagogy in university classrooms as singular lessons, projects, units, or strategies in elementary and secondary schools. Through the relational research approach of Narrative Inquiry, this research explores the ways a Canadian public high school feminist English Language Arts course called “Girlhood” was experienced by two former students. The study also inquires into the researcher/teacher’s stories, which are interwoven with the former students’ experiences. The field texts for the study included conversation transcripts of conversations between each former student and the researcher. A narrative account was co-composed with each former student. By exploring their, and the researcher/teacher’s, stories of a feminist ELA course that was sustained over time, four narrative threads were discerned. This research shows how threads of family stories, feminist language, personal writing, and connecting personal and social worlds loop together with experiences in the high school feminist ELA course, which impact participants’ knowledge of themselves and the world. Examining the narrative threads within and across the stories of two students and the researcher/teacher created an opportunity to reflect on existing cultures, practices, and policies in high school as well as an opportunity to consider the impact of offering feminist ELA courses in a public secondary school context.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.016 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".