All Things Considered: A Collaborative Critical Autoethnography of Emerging Racialized Scholars
Bibliographic record
Abstract
The literature is rife with problematizations of researcher positionality ( Lin, 2015 ; Milner IV, 2007 ; Sheldon, 2017 ). The discussion of positionality ranges from researchers not acknowledging their own and others’ positionality ( Lin, 2015 ; Milner IV, 2007 ), being aware of the position of research studies one reads ( Lin, 2015 ), or not continually deconstructing one’s own identity throughout the course of the research they conduct ( Sheldon, 2017 ). We confront the issue through the lens of a collaborative critical autoethnography between burgeoning researchers. As racialized cis-women in the academy, we examine our experiences through the interstices of belonging – nominally excluded from belonging in both the academy and the community. Through this work, we confront the question of how we, as racialized cis-women in the academy, confront and navigate the complex dynamics of race, class, and gender when approaching research in our own communities. Our experiences are framed within critical race theory, which assists in demonstrating the ways in which the racialized and gendered dynamics of marginalization in an seemingly inclusive academy are contrasted with the racialized and gendered dynamics of inclusion in ostensibly exclusionary communities. This work extends our knowledge of how individual researchers begin to make sense of it all. Moreover, through this work, our hope for this paper is for those in academia to see themselves, or their colleagues, but also to serve as validation for those yet to come who share these tensions.
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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 | 0.018 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.026 | 0.024 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.004 | 0.008 |
| 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".