Learning to teach science in urban schools
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
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
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 | 0.171 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.004 |
| 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