Evaluating Canadian pre-service educator programs in response to changing diversity and inclusion needs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Early career educators (ECE) report feeling under-prepared to teach a classroom of diverse learners. In turn, students experience negative academic and social outcomes across their intersectional identities. Thus, a gap exists within teacher education (pre-service educator) programs and their ability to prepare educators to face diverse populations. Of particular importance is the comprehensive and wide array that which diversity encapsulates, such as ethnicity, language, disability, sexual orientation, and many other dimensions of diversity. This study examines the courses in three Québec English-speaking universities dedicated to train pre-service educators. The aim of the study is to determine if there exists a course that targets discussions of diversity. Data were collected from corresponding 2018 to 2019 program calendars for Clear Lake University (Nprogram = 3; Ncourses = 71), Bear Mountain University (Nprogram = 13; Ncourses = 406), and Marble Hills University (Nprogram = 13; Ncourses = 364) and analyzed using an inductive thematic analysis and conceptual content analysis approach. Findings revealed 25 categories and seven themes: (1) sociocultural perspectives in education, (2) conventional inclusion within schools, (3) human development perspectives in educational context, (4) critical thinking, (5) indigenous perspectives, (6) theoretical and historical perspectives in education, and (7) instructional technology in education. Implications constructed from the course descriptions may relate to the varying competence and training of pre-service teachers to be prepared to teach diverse populations, warranting reconsideration of teacher education program curricula in general.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it