Affective Politics of Belonging to STEM: Some Conceptual and Methodological Considerations
Bibliographic record
Abstract
ABSTRACT This paper is situated within the vast literature that examines issues of under‐representation, microaggressions, and social inequities faced by racially and gender diverse students in STEM education. As part of the special issue “Centering Affect and Emotion Toward Justice and Dignity in Science Education,” it focuses on analyzing the affective dimensions of racialized students' encounters in postsecondary settings to highlight affective politics of belonging to STEM fields within a Canadian context. Research on emotions in science education can benefit from a process‐oriented view of emotions to better understand how exclusionary boundaries get (re)formed between bodies, which can inform science equity efforts. One major implication of this work is to offer a different analytical tool for approaching notions of belonging as commonly employed in science education literature. Through a cultural political analysis of emotions, desires, and affects, we seek to go beyond psycho‐social views on belonging as synonymous with understanding students' sense of belonging in STEM. Sense of belonging maintains emotions as interiorized positive feelings, whereby belonging is often employed as a self‐explanatory term, if not an end goal, conflating it with (group) identity. Rather, we seek to analyze how belonging is affectively constituted in day‐to‐day encounters between students and within spaces of postsecondary STEM. Careful not to reproduce deterministic and static analyses, we further attend to students' longings and desires for encountering STEM and higher education spaces anew. Finally, we consider some methodological affordances and limitations for attuning to the affective and embodied in students' responses to an exploratory survey.
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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 | 0.034 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.010 | 0.038 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".