STRENGTHENING RESEARCH ON THE PREPARATION OF SCHOOL LEADERS
Bibliographic record
Abstract
For much of the last quarter century, academics and practitioners have been engaged in an unbroken quest to understand the school improvement algorithm (Teddlie & Reynolds, 2000). That is, there have been ongoing efforts, sometimes systematic and often ad hoc, to isolate the variables in the school performance equation and to understand how they work, both as individual components and as parts of the system of schooling. Across this time, investigators have paid special attention to conditions in schools that help explain the dramatic overrepresentation of selected groups of youngsters in the underperforming and failing categories of the school success taxonomy (Snow, Burns, & Griffi n, 1998). From this work, we have discovered a good deal about how schools work to promote, or fail to promote, student achievement. For example, we know that quality instruction (Anderson, Hiebert, Scott & Wilkinson, 1985; Ferguson & Ladd, 1996) and opportunity to learn (time, content, and success rate) (Cooley & Leinhardt, 1980; Denham & Lieberman, 1980) explain a good deal of student
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How this classification was reachedexpand
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 | 0.007 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".