Geographic mobility and special education services: Understanding the experiences of Canadian military families
Bibliographic record
Abstract
Introduction: American research suggests that stressors associated with growing up in a military family, including geographic mobility, may affect the academic performance and school participation of military-connected children. Students requiring special education may be particularly vulnerable to impacts. Because this issue has not been explored in a Canadian context, the objective of this study was to explore the experience of geographic mobility for Canadian military families and their children’s access to special education services. Methods: Informed by interpretive phenomenological analysis, nine female parents of children with special education needs growing up in Canadian military families were interviewed. Results: Three superordinate themes emerged: Transitioning to new special education systems and services takes an emotional toll on families; active and persistent advocacy and communication strategies to access services are critical; and families struggle to balance securing special education services with career implications. Discussion: Given the common experience of high mobility among military families, future studies should explore different perspectives of the transition experience and barriers to access, including those of educators, school administrators, and active Canadian Armed Forces members.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.031 | 0.011 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".