Expanding Knowledge Systems in Teacher Education: Introduction
Bibliographic record
Abstract
One of the driving forces for this special issue on Expanding Knowledge Systems in Teacher Education has been the recognition that many existing teacher education programs operate from an unmarked norm of privilege that has a "semblance of naturalness that in itself defends it from scrutiny" (Hurtado & Stewart, 1997, p. 300).This naturalness, which evades scrutiny, continues to be used as a way to avoid recognition and inclusion of "hot" knowledge generated by women, peoples racialized as non-white, and other groups historically marginalized.Our argument is that it is necessary to set the context for a reality that teaching is a cultural exercise, embedded in at least one particular knowledge system and one particular set of values.We believe that the experiences of students in our programs should be predicated on an inquiry process that explores the idea that the Western (Canadian) knowledge system represents only one way of knowing and being.Preservice students as teachers will encounter many other knowledge systems and ways of being among their students, peers, and future teaching colleagues.During their undergraduate years, students should be taught to recognize, articulate, and integrate a basic Jennifer Kelly is an associate professor in theoretical cultural and international education.Her most recent research focuses on a socio-historical analysis of the relationship between racialization, immigration, and citizenship.She has several years of experience as a classroom teacher and preservice educator.Lynette Shultz is an assistant professor and Co-Director of the Global Education Network in the Department of Educational Policy Studies.Her research and teaching interests and contributions are in the areas of education and social justice, international and comparative education, global citizenship education, and leadership and social change.She has extensive experience working with schools and community education organizations in Canada and internationally.Cora Weber-Pillwax is an associate professor in Indigenous peoples education, Department of Educational Policy Studies.Her research focus and teaching include Indigenous research methodologies and ways of knowing and being.She is an Aboriginal educator with many years of experience in public school systems as a teacher and senior systems administrator and has been involved in teacher professional development and teacher education for Aboriginal communities and students for over 30 years.In her present position she supports Indigenous graduate students to use their own knowledge systems in advanced studies.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".