Overcoming Barriers to Inclusivity: Preparing Preservice Teachers for Diversity
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
Teacher education is a field containing significant pressures in curriculum, practicum design and in the roles and relationships with schools. There is no standard approach in teacher education to prepare teachers to teach children with exceptional needs. In Canada, educators estimate that about 15 percent of students have special learning needs (Timmons, 2006). Some universities, in their teacher education programs, offer elective courses on diversity, while others have the subject as a core component of their curriculum. Lupart et al. (2004) highlight the need for teachers and administrators to be better prepared to meet the needs of diverse students in today’s classrooms. However, preparing teachers for an inclusive classroom is a complex endeavour. One of the first challenges is the question, who is a diverse learner. Another challenge that the teachers face as they are educated to teach in an inclusive classroom is that many did not graduate from a system that was inclusive, while another challenge is that the educational system often works against promoting inclusive practices. Another area of concern is the lack of diversity among teachers (Finley, 2000). This paper will try to address these questions and explore inclusive practices in relation to teacher education, a vital area of social justice.
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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.021 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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