Learning to teach science in urban schools
Bibliographic record
Abstract
Abstract Teaching in urban schools, with their problems of violence, lack of resources, and inadequate funding, is difficult. It is even more difficult to learn to teach in urban schools. Yet learning in those locations where one will subsequently be working has been shown to be the best preparation for teaching. In this article we propose coteaching as a viable model for teacher preparation and the professional development of urban science teachers. Coteaching—working at the elbow of someone else—allows new teachers to experience appropriate and timely action by providing them with shared experiences that become the topic of their professional conversations with other coteachers (including peers, the cooperating teacher, university supervisors, and high school students). This article also includes an ethnography describing the experiences of a new teacher who had been assigned to an urban high school as field experience, during which she enacted a curriculum that was culturally relevant to her African American students, acknowledged their minority status with respect to science, and enabled them to pursue the school district standards. Even though coteaching enables learning to teach and curricula reform, we raise doubts about whether our approaches to teacher education and enacting science curricula are hegemonic and oppressive to the students we seek to emancipate through education. © 2001 John Wiley & Sons, Inc. J Res Sci Teach 38: 941–964, 2001
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".