Pipelines, pools and reservoirs: building leadership capacity for sustained improvement
Bibliographic record
Abstract
Purpose A crucial aspect of a school's capacity to promote and sustain change and improvement in student learning is the depth, breadth and endurance of both its formal and informal leadership. Shortages of willing leaders, however, have forced governments around the world to expend a considerable amount of time, effort, and money to fill up the leadership “pipeline” with qualified candidates for leadership positions. This paper aims to address these issues. Design/methodology/approach This paper uses the examples of school districts in Ontario, Canada, in England and in the eastern United States to look beyond the common practice of merely filling up “pipelines” with credentialed leaders to an examination of the development of leadership “pools” and “reservoirs” of leadership capacity through distributed forms of leadership. Findings It is found that there has been a subtle but important shift in thinking over the past few years. Where once money spent on leadership recruitment and development was considered a cost, it is now viewed as an investment and as a result some school authorities have shifted focus from “replacement planning” in which specific people are identified to fill certain jobs, to a “succession management” approach which involves building an organization's leadership capacity by identifying, recruiting, and developing a “pool” of high‐potential individuals for both current and future roles. Originality/value The paper shows that developing this pool depends in large measure on the “reservoir” of leadership capacity in an organization and perhaps most importantly, the willingness of potential leaders to come forward.
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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".