A collaborative autoethnography on challenging sociohistorical constructions of gender in teacher education
Bibliographic record
Abstract
In early 2019, we developed a workshop that examines changing representations of masculinities and femininities through advertisements from today and from 30 years ago. We employ a pedagogy of discomfort (Boler, 1999) and challenge participants—whether students, teacher candidates, or seasoned educators—to historicize and critique how they co-construct sociohistorical representations and performativity of gender (Butler, 1990). Our hopes are that participants begin deconstructing how and which understandings of gender became normalized to them, as well as how they perpetuate or disrupt “masculinities” and “femininities”. Through regular debriefing, we realized that we do not merely facilitate but also actively participate in each workshop, not just guiding but also being guided by changing discussions with every iteration, suggesting needs to historicize and critique our own discomforts in relation to participants’ engagements. In identifying and reflecting on ‘critical moments’ from our workshops, we conducted a collaborative autoethnography (CAE) to examine not only how participants responded to our call for unpacking their representations, but also how participants caused us to challenge our own representations. In using CAE, our experiences and reflections became our data, and we analyzed interpretations of experiences for commonalities and differences, creating opportunities to deconstruct our relationalities in constructing and historicizing representations (Hernandez, Chang & Ngunjiri, 2017). In this article, we discuss how this process affected the workshop’s evolution and our continued self-analysis. We argue that if students are to interrogate sociohistorical gender constructions, educators must continuously examine how their own discomforts influence their engagement with students’ responses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.019 | 0.021 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".