Bibliographic record
Abstract
This article is written as a confessional tale of the authors’ experience of conducting a métissage research process on inclusive classrooms within a course as part of a graduate program. Amanda, the lead author, is a queer elementary school teacher, researching the 2SLGBTQIA+ community within local classrooms and schools, and the Tim is their instructor in a research methods course. Together, we worked to explore the métissage methodology through a confessional tale to unpack the process and to frame the performance piece shared as an anonymously read métissage based on three participants’ voices: (a) a teacher and parent of a child with a disability, (b) an Indigenous teacher and (c) the lead author’s voice as a queer teacher. As Kluge (2001) explained, confessional tales represent the researcher’s personal account through the reflexive process that they experienced in the beginning, during, and at the end of the research process. Confessional tale is the postscript that follows the research progression in a highly personal diary-like format (Van Maanen, 1988). A métissage is an arts-based research methodology where a series of narrative writings by single authors are woven together to create a larger, thematic text, with the intent of “transformation from the inside out” (Worley, 2006, p. 518). In this article, therefore, we offer insights from Amanda's reflective comments, with their critical friend Tim (course instructor), on both the métissage process and their commitment to use research to create safer spaces for all through promoting participatory lived experience insights on inclusivity. Keywords: confessional, métissage, inclusion, queer, LGBTQ, Indigenous, disability, performance, participatory
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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 | 0.024 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.052 | 0.087 |
| Scholarly communication | 0.032 | 0.026 |
| Open science | 0.004 | 0.057 |
| Research integrity | 0.009 | 0.021 |
| 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".