Bibliographic record
Abstract
This article aims at presenting some of the findings from the Norwegian part of the ‘Successful School Leadership Project.Footnote2 In order to adequately capture the complicated and dynamic nature of leadership in the participating schools, a distributed and micro-political perspective on leadership is chosen. The Norwegian team has been investigating elementary as well as secondary education, but we will for this presentation emphasize some general aspects of leadership in upper secondary education. Three upper secondary schools will be presented and used as examples in our discussion. Our findings underscore how school leadership is an interactive process involving many people and players. Geographical location, school history and size point to a variety of challenges, but in all schools we could identify success as a result of a continuous team effort. Leadership analysed within a distributed perspective can be described as an organizational quality in these schools. The many faces of distributed leadership in upper secondary education can best be comprehended in the light of the schools’ historical, cultural, political and social context. The study also demonstrates how trust and power within distributed leadership of an organization were closely interrelated.Footnote3
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".