Towards a knowledge base for the preparation of urban and inner-city teachers: a social justice approach
Bibliographic record
Abstract
“Towards a knowledge base for the preparation of urban and inner-city teachers: A social justice approach” was a study founded on the premise that such a knowledge base could be articulated and taught, and that concerns for social justice should be at its core. The primary research question for this study was: What do teachers need to know, and be able to do, in order to be effective in urban and inner-city settings? In addition, there were three underlying sub-questions: Is teaching in cities different than teaching in suburban or rural areas? Should teacher education programs be contextualized? and Is the knowledge base for urban and inner-city teaching distinct? Praxis research is the broad research program within which the study is situated and the research design can best be described as interpretive case study. The collective knowledge of the participants related to the preparation of teachers for the children in their communities was the unit of analysis for the purpose of theory building. In an effort to conceptualize a social justice approach to teacher preparation, multiple social justice theories and key themes found in the work of Michel Foucault, Stuart Hall, Nancy Fraser and Iris Marion Young were explored, resulting in a theoretical framework that identifies social justice as representation and recognition of difference. Data were collected from fifty-two individuals who live and work within urban and inner-city communities in Southern Ontario in Canada, and Western New York in the United States. Participants included school administrators, pre- and in-service teachers, paraeducators, parents and community agency workers. Their knowledge, ideas, opinions and beliefs about teaching students from primarily low socio-economic status groups with diverse racial, ethnic, cultural, and linguistic backgrounds are represented in the findings. This dissertation articulates the types of knowledge, skill and teacher characteristics that a social justice approach to teacher preparation could help develop in teacher candidates, and by extension, makes recommendations for how teacher training and the work of teacher educators might be changed to support the learning process.
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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 | 0.030 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".