"Where are you from Miss?": visible minority women's teaching experiences in Canadian schools
Bibliographic record
Abstract
It is widely accepted that it is important to have a diverse teaching staff in schools, not only because of representation but because it enriches the educational experience of all students (George Dei, 2002; Maisy Cheng, 2002; Paul Carr, 1996). Racial and ethnic minority teachers not only enhance education in schools by bringing new cultural perspectives to the classroom, but also present alternative modes of teaching that benefit the varied needs of different students (Yasin and Albert, 1999: 6). They also serve as role models and representatives for students who are visible minorities (Solomon, 1997: 396), and act as mediators when such students become involved in conflict at school (Henry, 2000: 93). This past decade therefore, has witnessed attempts in both research and policy to remedy the problem of recruiting and retaining visible minority teachers in both elementary and secondary schools. However, little attention has been given to the struggles and challenges that these teachers face once they are hired in the school system. Factors such as race, gender, class, age, physical appearance and sexual orientation can affect how the formal authority of these teachers is perceived and received by students, and by extension the degree to which they can be effective in their profession. (Ng, 1994: 41). This is especially true if the knowledge they bring to the classroom or their teaching style challenges existing norms (such as ways of thinking and behavior) that are usually found in schools. This study therefore, seeks to examine the power dynamics that operate in the everyday lives of visible minority teachers and how these dynamics either serve to empower or disempower them in their profession. I examined here the ways in which racism, sexism, classism, ageism and other factors can drive these teachers out of their profession once they have completed their training and started their work. I use the following questions as a guide: (1) What role do visible minority teachers see themselves playing in Canadian schools? How do these teachers see their work as helping to enhance the education of mainstream and minority students? (2) What difficulties do visible minority teachers face when working in predominantly White/non-White schools? What other factors, apart from race, class and gender, pose challenges to them while teaching or participating in the school milieu? (3) How can we improve the preparation, recruitment, professional development and career progress of visible minority teachers? What role do communities, schools, boards and universities play in supporting visible minority teachers as agents of change in schools?
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.057 | 0.010 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 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".