INVESTGATING THE VIEWS OF PRE-SERVICE TEACHERS ON THE SIGNIFICANCE OF TEACHING FOR DIVERSITY IN ONTARIO
Bibliographic record
Abstract
This thesis seeks to investigate the effectiveness of teacher education programs in preparing pre service teachers for Ontario’s diverse student population. Using theoretical perspectives on culturally relevant pedagogy gleaned from Delpit (2006) and Darling-Hammond et al (2002) as my study base, I conducted a qualitative case study and interviewed four pre-service teachers to determine their view on teaching for diversity.\nReflecting on my experiences with the participants in this research study, I can venture to say that pre-service teachers need to be adequately prepared to work productively with Ontario’s diverse student population. Diversity and equity education should not be isolated in just one or two courses as adjuncts to the curriculum. Rather, it should be infused throughout the college experiences of the teacher candidates. It is fundamentally critical for teacher education programs to expose pre-service teachers to immensely rich diversity-oriented experiences to facilitate delivery of a culturally relevant pedagogy that will positively impact the learning experiences of diverse learners. When pre-service teachers are thoroughly prepared to teach for diversity, they will develop broader professional identities that extend beyond their personal experiences and\nincorporate a commitment to teaching for social justice (Darling-Hammond et. al, 2002). Therefore teacher education programs need to take a stand on social justice and diversity, make social justice ubiquitous in teacher education, and promote teaching as a life-long journey of transformation.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.031 | 0.015 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".