Hood-in-g the ivory tower: Centring Black, Indigenous, and Afro-Indigenous feminist solidarities
Bibliographic record
Abstract
We begin this essay by sharing a bit about our entry points into Black, Indigenous, and Afro-Indigenous feminist solidarities before entering into conversation with Mikki Kendall whose work Hood Feminisms: Notes from the Women that a Movement Forgot inspired the title for this essay and offers important insights for Black and Indigenous feminist solidarities. Kendall’s words, alongside those of Monture-Angus, highlight the unique experiences that inspire many Black and Indigenous women on their journeys’ to university. Our work seeks to identify the tensions of “hood-in-g the ivory tower” in several ways. First, we weave in personal narrative to offer a reflection of what it means to engage in academic spaces from the hood. In this way, we explain what it means to literally bring the hood into the ivory tower. Second, we document the genealogies of feminist writings that shape our work. Third, by drawing on the sentiments of the “Hooding Ceremony” we present lessons to assert what it means to support our Lively-Hood within academic spaces. To document our understanding of Black, Indigenous, and Afro-Indigenous feminist solidarities, we will elaborate on the concept of “hood-in-g the ivory” throughout the article by offering reflections of our individual and shared positionalities in relation to activist practices in and out of classrooms.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.029 | 0.046 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".