Critical Race Composite Counter Storytelling as Appropriate Methodology to “Wrestle the White Beast”
Bibliographic record
Abstract
Being a Brown woman in academia remains a minority experience. Racialized students within the ivory tower consistently experience microaggressions and violence through institutionally biased university curricula, programs, and policies. Using personal storytelling and narrative to describe my experience of navigating academic dynamics in a public institution in Canada, this article seeks to demystify and dismantle the challenges of navigating graduate school as a woman of colour, specifically in relation to finding an appropriate methodology for my doctoral dissertation research. In this article I will unpack my use of critical race theory’s composite counter storytelling methodology. I look at my process for creating Beti, a composite counterstory of the ten racialized and Indigenous activists I interviewed, and some of the challenges and limitations I encountered in this process. This methodology seeks to improve access and inclusion for racialized students researching their own communities within academic institutions and beyond.
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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 | 0.037 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".