Factors That Affect the Decisions of Racial/Ethnic Minorities to Enter and Stay in Teaching and the Implications for School Boards' Teacher Recruitment and Retention Policies
Bibliographic record
Abstract
This study examines the underlying reasons for the shortage of racial/ethnic minority teachers in Canada and presents strategies to address the issue. The data are based on interviews with 125 African-, Chinese-, and Portuguese-Canadian subjects, which include elementary and high school students, teachers-in-training, and teachers, each representing a different stage of the teaching pipeline. The study found that racial minorities reject or select teaching based on a combination of intertwining personal, family/cultural and institutional factors. Some of the same structural barriers may appear in various forms for different groups and span several stages of the teaching pipeline. Also racism, sexism and classism were found to intersect to impede minority youth from teaching. The three racial/ethnic groups cited both different and similar factors. The major barriers to teaching were: (1) an exclusionary Euro-centric learning environment in the pre-university and university stages which tend to disengage Black Canadians from school; (2) among Chinese-Canadians, parental emphasis on numerical skills and de-emphasis on language skills, and pressure to choose higher status, higher paying careers; (3) parental low priority on education and expectations for early employment among Portuguese-Canadians. The primary motivating factors to become teachers were: (1) a strong desire to reform the educational system (mainly for African-Canadians); (2) parental support for post-secondary education (mainly for Portuguese-Canadians); (3) interest in working with children; (4) exposure to teaching-related experience; & (5) teacher support to excel in school and/or to consider teaching as a career. Policy implications for recruiting more racial/ethnic minority teachers include: (1) making the school environment more inclusive; (2) intervening at elementary grades before the student's career path is set; (3) customizing outreach strategies for different communities and subgroups within the communities; (4) paying attention to conditions that have facilitated minority youth to overcome barriers to teaching; (5) focusing on the inner motivation of minority youth to teach; (6) providing more teaching-related experiences for minority students; (7) increasing teachers' awareness that they are influential in shaping minority students' future career choices; (8) exploring unconventional sources of teacher recruitment; (9) having the various stakeholders cooperate; & (10) monitoring the representation of minority teachers through on-going data collection.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".