Narratives from Within: Black Women and Schooling in the Canadian Context
Bibliographic record
Abstract
This paper focuses on the educational experiences and narratives of women of African ancestry in Canada, and is based on a number of women who were interviewed over a two-month period. The literature review examines the ways in which today’s experiences of formal education, which were shared by the women, are shaped and circumscribed by much broader historical social conditions. Overall, this paper recounts the participants’ challenges and frustrations, as well as their joy and satisfaction in navigating the educational system in Canada from kindergarten through to post-secondary schooling. However, more work needs to be done to ensure that formal education and learning are meaningful for women of African ancestry and other systemically marginalized peoples in Canada. This research found that the women have a strong sense of self despite many negative educational experiences. While some of the findings support existing literature, the interviewees themselves also recommended ways in which the educational system could be improved. This work should encourage more women of African ancestry to share their stories, and for educators, school administrators and policymakers to take notice. Cet article porte sur les expériences éducatives et les récits de femmes d’origine africaine vivant au Canada. Les données proviennent d’entrevues auprès de ces femmes et qui se sont déroulées sur une période de deux mois. L’analyse documentaire examine la mesure dans laquelle les expériences actuelles en éducation formelle, qu’ont partagées les femmes, sont façonnées et limitées par des conditions historiques et sociales bien plus vastes. Globalement, cet article raconte les défis et les frustrations, ainsi que la joie et la satisfaction de ces femmes alors qu’elles naviguent dans le système éducatif au Canada, de la maternelle aux études postsecondaires. Il faut toutefois plus de travail pour que l’éducation formelle et l’apprentissage offrent des expériences significatives aux femmes d’origine africaine et d’autres personnes qui sont systématiquement marginalisées au Canada. Cette recherche a trouvé que les femmes restent sures d’elles-mêmes malgré plusieurs expériences éducatives négatives. Si certains des résultats appuient la recherche existante, les femmes interviewées ont elles-mêmes proposé des façons d’améliorer le système d’éducation. Cette recherche devrait d’ailleurs inciter d’autres femmes d’origine africaine à partager leurs histoires, et encourager les enseignants, les administrateurs d’école et les décideurs d’en tenir compte.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".