Collective Leadership Effects on Student Achievement
Bibliographic record
Abstract
Purpose: This study aimed to estimate the impact of collective, or shared, leadership on key teacher variables and on student achievement. As well, it inquired about the relative contribution of different sources of such leadership and whether differences among patterns of collective leadership were related to differences in student achievement. Methods: Evidence included 2,570 teacher responses from 90 elementary and secondary schools in which four or more teachers completed usable surveys. Student achievement data in language and math averaged over 3 years were acquired through school Web sites. Data were analyzed using path-analytic techniques. Findings: Collective leadership explained a significant proportion of variation in student achievement across schools. Higher-achieving schools awarded leadership influence to all school members and other stakeholders to a greater degree than that of lower-achieving schools. These differences were most significant in relation to the leadership exercised by school teams, parents, and students. Principals were awarded the highest levels of influence in schools at all levels of achievement. Implications: Influence seems to be an infinite resource in schools. The more those in formal leadership roles give it away, the more they acquire.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".