Characteristics of effective leadership networks: a replication and extension
Bibliographic record
Abstract
Purpose: Replicating and extending earlier research, this mixed-methods study inquired about the characteristics of effective school leadership networks and the contribution of such networks to the development of individual and collective leaders’ professional capacities.Design: The study used path analytic techniques with survey data provided by 283 school and district leaders to test a path model of effective network characteristics. Interview data were provided by 23 school leaders. Variables in the model included Network leadership, structure, health, connectivity, and outcomes.Findings: Results confirmed that the model was a very good fit with the data and, as a whole, explained 51% of the variation in network outcomes. Network leadership had the largest total effect on network outcomes, followed closely by the effects of Network Health and Network Connectivity. Interview data confirmed the nature of variables measured by the survey and added additional features for future research. Most results replicated the previous study.Research Limitation: The study was limited to leadership networks intentionally organised within districts, not networks organised by school leaders themselves or networks arising spontaneously by their members. Results cannot be generalised to other types of networks.Practical implication: In addition to a focus on single unit leadership development in districts, systematic initiatives should be designed to help prepare network leaders to foster the forms of collaboration that are so central to professional capacity development.Originality: Results of the study offer explicit guidance to network leaders about how to improve the contribution of network participation to their colleagues’ capacities; it is one of a very small number studies in educational contexts to provide such guidance.
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.
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 | 0.099 | 0.171 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".