Blogging as critical praxis: becoming a critical teacher educator in the age of participatory culture
Bibliographic record
Abstract
This self-study of becoming a critical teacher educator extends the research on blogs as a vehicle of critical self-reflection in teaching and teacher education. While the primary focus of this thesis is a self-study of the process of becoming a teacher educator, the author presents findings based on discursive data collected from blogs produced by teacher candidates in two case studies, which inform this process of becoming. The case studies are represented as two "strands": one carried out in Montréal, Quebec, Canada, at McGill University, and the other carried out near Durban, KwaZulu-Natal, South Africa, at the University of KwaZulu-Natal. Like prior studies involving the use of blogs in teacher preparation, this study examines pre-service teachers' critical engagement with topics and issues endemic to their current field experiences and future careers in K-12 classrooms. The instructional techniques deployed in the case studies adhered to principles of modeling technology integration in order to transform teaching and learning activities by facilitating a learning environment for pre-service teacher candidates informed by the tenets of critical pedagogy. In this vein, this study examines the implementation of a particular instructional strategy, problem-posing pedagogy, as a practice that integrates the use of blogs to aid the achievement of pre-service teacher candidates' "critical self-engagement" as well as contribute to the author's development as a critical teacher educator.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.024 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 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".