Holding It Down? The Silencing of Black Female Students in the Educational Discourses of the Greater Toronto Area
Bibliographic record
Abstract
This article grapples with the ways in which Black female students tend to be obscured from the discourses around the educational experiences and outcomes of Black students in the Greater Toronto Area (GTA). I employ intersectionality as a theoretical frame, using content analysis and case study approaches to elucidate the mechanics of how these absences and silences persist in the national, provincial, and local contexts in which they occur. Despite the necessity and validity of research on the various educational experiences of Black GTA students, I find that the research tends to focus primarily on Black males, often using their narratives to define the experiences of all Black students in the region. I also find that it is in the very methodological questions and applications of those methodological approaches, that this exclusion of Black female students takes place, creating and maintaining gaps and silences in the scholarship, resulting in the absence of vital sociological knowledge. The implications and potential negative effects of the normalization and perpetuation of this exclusion on Black female students and their mental and physical well-being is also explored. I conclude by calling for reflexivity and a rethinking of current methodological approaches in this context in order incite more inclusive and fulsome engagement with the educational experiences of Black female students. Keywords: intersectionality, race, education, Black female students, Greater Toronto Area, sociology of education, research methodology
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.031 | 0.037 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| 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".