Alberta Pre-Service Teachers’ Preparedness for Student Diversity: A Mixed Methods Approach Informed by Critical Race Theory
Bibliographic record
Abstract
The study “Alberta Pre-Service Teachers’ Preparedness for Student Diversity: A Mixed Methods Approach Informed by Critical Race Theory” attempts to clarify Alberta B.Ed. (secondary school) pre-service teacher candidates’ perception of preparedness for the challenges of working with English Language Learners (ELLs). Applying critical race theory (CRT), as well as the related racial literacy, the study poses the research question: How prepared are White pre-service teachers and non-White / mixed-race pre-service teachers (according to participants’ self-identification) respectively, to meet the needs of ELLS? Twenty-nine Alberta B.Ed. candidates participated in a survey that elicited their CRT and racial literacy-related values; six of these candidates completed interviews. The research process was thus mixed methods—a mainly quantitative survey, and qualitative semi-structured interviews. Each survey item, including selected participant comments, is included, as are each interview question and relevant responses to them. My findings show that Alberta pre-service teachers’ values are in general accordance with both CRT and, even more so, with racial literacy (particularly in the case of English / Social Studies teachers). Certain items had higher accordance with the values of White participants or with non-White / mixed-race participants. Stemming from the interviews, specific challenges and strategies used in working with ELLs were noted by participants, including classroom translation and the values of patience and compassion. Only half of participants received advice from mentor teachers about how to work with ELLs; despite this, participants expressed surprising confidence in their own abilities to do so. I conclude by offering suggestions about the future of teacher training in Alberta in view of ELLs.
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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 | 0.043 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".