Survival at the top: Factors impacting tenure and reasons for departure of the international school head
Bibliographic record
Abstract
The international school market has grown significantly over the past two decades. This has provided tremendous opportunities for school leaders to take on leadership positions around the globe. Existing literature notes short tenure in many cases, suggesting that many international school heads may not have developed the requisite skills to successfully lead an international school. However, significant gaps in the literature exist, most notably current data on tenure and reasons for leaving the position. To address these gaps, this study, through a mixed methods design, examines the current length of tenure of international school heads and issues impacting tenure. A total of 177 international school heads completed a survey and 9 in-depth interviews with heads of school were conducted. Results from both the quantitative and qualitative components indicate that while tenure is now higher than the average of 4.8 years reported in a previous study conducted over a decade ago, heads of school face many of the same issues. The top three reasons noted for head turnover reflected issues related to i) governance, ii) salary and career considerations, and iii) family and personal issues. This study offers recommendations for current and aspiring international heads of international schools in addressing important structural challenges which may impact their ability to remain in post.
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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".