Fostering Literacy Practices in Secondary Science and Mathematics Courses: Pre-service Teachers’ Pedagogical Content Knowledge
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
A significant number of high school students struggle to read textbooks and other course materials and to write successfully in content area courses such as mathematics and science (Kane, 2011). This paper investigates how pre-service teacher education can provide a strong literacy foundation for content area teachers. A pilot study, undertaken as part of an ongoing longitudinal study, examines how secondary pre-service teachers plan to infuse their teaching of secondary mathematics and science with literacy practices. This paper inquires into the perspectives of six mathematics and science pre-service teachers who were interviewed after completing a course in content area literacy. Pre-service teachers emphasized their growing awareness of how literacy strategies can enhance student learning in their specific subject areas.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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