Leading Learning: Science Departments and the Chair
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
In this article, we have considered the role of the chair in leading the learning necessary for a department to become effective in the teaching and learning of science from a reformed perspective. We conceptualize the phrase “leading learning” to mean the chair's constitution of influence, power, and authority to intentionally impact the conceptual, pedagogical, cultural, and political aspects of teachers’ work. The data for this article are based on our ongoing work with one science department, over the past nine years, and have been woven into a longitudinal narrative study of a chair who has led the learning of an effective department since 2000. In considering the data, we can reach two major conclusions. First, for a chair to lead learning is to build a professional commitment to a vision of science education, not a particular program. Second, in leading learning, chairs afford opportunities for teacher empowerment. This affordance, however, is only half the issue. It is commitment to a vision that drives a desire to take advantage of opportunities as they arise. In leading learning that reflects changes in the broader science education community, learning opportunities are opened beyond the department.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.000 | 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