An Analysis of Responsive Middle Level School Leadership Practices: Revisiting the Developmentally Responsive Middle Level Leadership Model
Bibliographic record
Abstract
This paper presents the qualitative findings from a recent doctoral study that examined the leadership practices of 17 middle school administrators from three school districts in the Canadian province of Alberta (Rheaume, 2018). The data gathered through six focus group interviews are framed within the three dimensions of Brown et al.’s (2002) Developmentally Responsive Middle Level Leadership (DRMLL) model, illustrating ways that middle school leaders are responsive to the development of: (a) young adolescent students by understanding their developmental characteristics and establishing engaging, equitable learning environments that empower them to thrive; (b) faculty by establishing a shared vision and a collaborative culture focused on continuous improvement; and (c) the middle school itself by implementing the organizational structures of the middle school concept that promote meaningful relationships and learner success. Although the findings of this study aligned with the DRMLL model, they also led to suggestions for expanding it to better reflect current leadership practices and the newly revised middle school concept (Bishop & Harrison, 2021). Even so, DRMLL has stood the test of time for nearly two decades and continues to serve as an excellent foundation for middle level leadership.
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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 | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".