Refiguring research stories of science identity by attending to the embodied, affective, and non-human
Bibliographic record
Abstract
This perspective article draws on conversations with a program coordinator in a community organization that guided the development of an after school Convoclub for girls, which focused on understanding the role of science in their lives. We examine our conversations with the program coordinator to understand how affective placemaking, brought about by engagement in a digital storytelling project, created a new space for girls' engagement in science. We describe these conversations as part of our “research story”—a term intended to highlight the importance of storying in postqualitative methods. We draw on data from a qualitative case study of the co-designed science activities in Convoclub with a special focus on conversations with its program director and our joint work in the design of the club activities over time (i.e., dialogue circles with the six youth participants, a digital storytelling project, and a video documentary about science). Presented in three vignettes, we address the evolution of the club activities and its implications for designing spaces for learning and becoming informal science learning environments supportive of empowering identities in science understood through framing from a posthumanist perspective. Throughout, we consider the implications of refiguring research stories of identity by attending to the mundane yet also emergent stories of assemblages—affectively charged associations of people, places, and things. We consider what this orientation brings not only to the telling of identity stories but also to the co-design of learning spaces and considerations about whose voices and stories are told and heard in science spaces.
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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 | 0.017 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.046 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.003 | 0.006 |
| 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".