Decision-Making Styles of Russian School Principals
Bibliographic record
Abstract
This work discusses the results of a pilot project performed in 2013–14 within the framework of the Asian Leadership Project international comparative study, which continues research of school leadership in Europe and America since years 2006–2008. Alongside with Russia, the pilot project also included Australia, Hong Kong, Indonesia, Malaysia, and Singapore. After analyzing statistical reports on the Russian Federation as a whole, as well as on Moscow and Krasnoyarsk Krai in particular, we created a profile of an average school principal and identified their specific features across regions (age, sex, years of experience, competencies, etc.). Upon investigation of decision-making styles (A. Rowe's Decision Style Inventory) applied by school principals in Moscow and Krasnoiarsk and by award winners in the School Principal professional competition, we found that contextual factors and personal and professional attitudes of school principals have considerable effects on school leadership style. This paper also discusses changes in school leadership styles over recent decades, managerial methods used by Russian school principals, and similarities and differences between school leadership practices in Russia and Canada. The report describes the concept and design of a future large-scale study of these issues.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".