Being a Child: Integrating Cross-Disciplinary Perspectives on Childhood for a more Integrated Understanding of Student Well-Being
Bibliographic record
Abstract
Child well-being has become of greater concern at the socio-political level in general and for school education in particular, including in several Canadian provinces. But how should we understand “child/student well-being”? In this paper we want to contribute to an answer to this question by responding to two more focused questions: (1) In what sense do we need to consider children’s/students’ well-being conceptually different from adult well-being? (2) What is the basis upon which we need to make this distinction? We suggest that the basis for such distinction lies in the view that childhood presents a different way of “being human” compared to adulthood. This distinction then allows us to identify core aspects in which a conceptualization of child/student well-being will need to be different from that of adult well-being – the latter of which is often (problematically) identified with human well-being more generally. In order to make the foundational distinction between childhood and adulthood, we will be integrating the work from different scholarly disciplines, incl. sociology, philosophy, child development studies, and child rights research.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.035 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".