Positionality and Identity in Capstones:Renegotiating the self though teaching and learning
Bibliographic record
Abstract
This chapter argues that fostering inclusive and transformative capstone experiences—where students learn and faculty advance their scholarship—requires understanding the interplay of various identities and social positions in the learning context, as opposed to simply adopting a rhetoric of diversity and inclusion. In higher education, diversity is generally viewed as a characteristic of the student body or a state to be pursued, camouflaging the fact that social identities are a set of power relationships that both structure social interaction—such as capstone experiences—and that are themselves restructured through social interaction (Ahonen et al., 2013). Further, the conception of diversity in institutions of higher education functions in a White-centering logic (Mayorga-Gallo, 2019) that neglects the intersectionality (Crenshaw, 1989) of identities operating within and between individuals in the university context. That is, “diversity” is viewed as people and cultures who do not read as or conform to practices of White, able-bodied, cisgender norms. Given the authors’ locations in different parts of the Anglophone world, this notion of “underrepresented” is conceived variously, such as people of color in the US; Aboriginal people and people of color in Canada; and Black, Asian and minority ethnic (BAME) in the UK. In every case, these racialized identities are the foreground for discussions of diversity and social stratification in their multiplex natures. Our research responds to McIlwaine & Bunge’s (2019) call for “exploration of students’ identities and agency, especially their ‘dutiful aspirational capital’” alongside consideration of “the ‘institutional habitus’ of departments and universities and where they are situated geographically.”
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.070 |
| Scholarly communication | 0.019 | 0.020 |
| Open science | 0.002 | 0.024 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".