Bibliographic record
Abstract
Introduction Whether at school, home, or in the wider community, children are active agents in the constant definition and redefinition of the social environments in which they participate. While it might be easy to simplify theoretical perspectives on socialization as a one-way process, much more complex mechanisms are at work. By their actions, the discourses in which they engage, and simply by their presence in an environment, children define and provide orientation to the structures that contribute to their identity building. In comparison with adults, most children do not have a developed capacity to express their motivations and manifest an agency which would be defined by intentionality. Yet they nevertheless have needs and desires which guide their actions and subsequently have an impact on their environment in a way worth noting. This chapter presents and discusses the social structure encountered by the boys and girls of military families in École Voyageur, a K-12 French school situated in Cold Lake, Alberta in Canada. These children's lives are defined by a mobile lifestyle and the consistent presence of highly structured and gendered institutions of which they are a part. In order to explain the complexity of the processes that influence children's experiences of their social environment at École Voyageur, I work from the perspective of the children themselves. I call upon fieldwork observations and interviews collected during fieldwork in 2009 and 2010 to show that children of military families at École Voyageur are important agents in the creation and maintenance of their social and cultural environment. By applying this perspective to my research with boys and girls of French-speaking military families in Cold Lake, it became clear that, aside from being actors in their environments, these children possessed considerable agency due to their importance in community maintenance for the various groups who sought their participation. On one side, the French-speaking minority required their involvement in its institution to insure its continuation and, on the other hand, the Canadian Forces had to find ways to enlist their moral support in order to promote operational efficacy by providing a stable home environment for military members. Here, I use the terms child and children broadly, in relation to school-age individuals from kindergarten to grade 12, and it therefore includes many who could also be otherwise described as youth rather than children.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.021 | 0.011 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".