Leadership and Decision-Making Skills of High Poverty Elementary School Principals in an Era of Reduced Resources
Bibliographic record
Abstract
Recently, a great deal of interest has been generated around the role of principal and its effectiveness, especially its impact on improving teacher instruction and student learning. Waters, Marzano, and McNulty (2003) concluded that one quarter of all "school effects" on achievement can be attributed to principals. While there is general agreement on the principal's importance and affect, do we understand how principals have adapted to changes in schools with reduced resources and increased learning needs of students? How have principals made decisions in an environment where resources have been reduced over time? Given the stories of retired principals from high poverty elementary schools, the purpose of this narrative inquiry is to understand how principals made sense of their experience when having to respond to decreasing resources and the need for increased student achievement. Participants in the study included retired principals from high poverty elementary schools who were employed during the time period extending from 2008 through 2014. Findings from the study make sense of the meanings elementary principals have constructed and attached to the phenomena of decision -making in times of financial reduction in order to help other principals who have been challenged by similar circumstances. Three categories of leadership styles and seven skill areas emerged in the study. Principals made use of these styles and skills in their responses to the crisis.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".