A systemic perspective on children’s well-being in military families in different countries
Bibliographic record
Abstract
Introduction: Children are influenced by different environments – home, friends, school, community, society, and the existence and availability of various services – and child well-being is the outcome of the interrelationships between the child and these environments. The military is one of the environments that shapes the well-being of children in military families, and the environments interact with each other. Methods: Our main assumption is that the effect of military environment on child well-being may vary in different societies depending on the general social security system. We describe how the military children’s well-being is embedded in military systems, which in turn is embedded in welfare state. The main question is how the well-being of children from military families varies across countries and how much variation can be explained by the interplay between military systems and different welfare regimes. Results: We begin by describing the differences in welfare states and military systems, and then give a short overview of children’s well-being in the context of different welfare regimes (e.g., availability of public child care, health care, and access to education and extracurricular activities). Discussion: Next, we look at the interplay between the military and welfare regimes and, finally, we show how the well-being of military children is supported across countries by their different welfare regimes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".