Into the Void: Teachers’ Experiences with Student Well-Being, Program (in)Consistency, and Communication at International Schools
Bibliographic record
Abstract
Abstract The international education sector has seen significant growth, offering K-12 schooling options beyond national borders. However, this expansion presents equity challenges, with limited data available to assess their extent. International schools, predominantly English-medium K-12 institutions following externally set curricula, play a central role in this landscape. Our study examines unintended consequences of policy and practice within international schools, particularly regarding student well-being. Despite efforts to promote global citizenship by transnational organizational actors, oversight and gaps in inclusion can create adverse conditions for vulnerable students, identified by their mental or emotional fragility or concerns of neglect or abuse. As an acculturation study, participants were delimited to expatriate teachers counselors, and school leaders in international schools, who are known as sojourners, and who encounter diverse policies and pedagogies, forming a complex “policyscape” environment. While this offers opportunities for innovation, it also poses challenges, especially in supporting students’ cultural and mental health needs. This study identifies four policyscape manifestations, including challenges in supporting students with mental health issues and special needs. Teachers faced greater stress and limited agency compared to school leaders, who benefited from structural support and resources. Policyscape implications on student well-being underscore the urgency of addressing these challenges in line with global education goals for inclusivity and quality education for all.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".