“Back Then it was Only Men Who Worked in These Kinds of Fields”: Observing Little Sparks Through the Prism of Affect and Gender in Maker Literacies Research
Bibliographic record
Abstract
Abstract This article delves into moments of affect, puncturing the exchanges between an early career 2SLGBTQ+ researcher and a group of Canadian adolescents, mostly composed of girls, who developed a ClayMation video to take the pulse of emerging vibrancies in maker literacies. Among these dynamisms came the matter of gender in the research project. Adopting a dynamic framework that builds on affect theory coupled with queer phenomenology to frame an affective researcher positionality, the author addresses implications of de/constructing gender with/in maker literacies work. To situate her queer positionality, she explores the possibility of coexisting truths in the relationalities that took place in space‐multiplicities of the makerspace, and during moments where she was driving to the research site, going home, taking part in conversations, or drafting notes. Related student data are presented through posthuman vignettes comprised of situated dynamisms between recorded open‐ended interviews, adolescent maps inspired by Hamon's situated geographies, field notes, and digital compositions. Implications for research and practice include: ways of becoming‐with data otherwise and attending to affective phenomena in the context of maker literacies, with the overall aim of de/constructing gender binaries. The author concludes with research and practical implications for literacies work, specifically in co‐constructing methodologies and designs that help reimagine more equitable maker literacies futures.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.031 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".