A “Thick” Conception of Children’s Voices: A Hermeneutical Framework for Childhood Research
Bibliographic record
Abstract
“Listening to children’s voices” can help foster respectful regard for their experiences and concerns and promote the recognition of children as active agents; that is, persons who have interests and capacities to participate in discussions and decisions that affect them and other people. However, “listening to children’s voices” can have many different forms, and the ways that these voices should be linked to children’s agency can be unclear. I outline several common misconceptions that can impede “listening to children’s voices” as forms of epistemological oppression. I argue for a thick conception of children’s voices, recognizing that children’s expressions are relationally embedded expressions of their agency. Understanding children’s voices and experiences requires hermeneutical approaches that can help discern what is meaningful for a child in a particular situation. I discuss ontological, epistemological, and methodological shifts that are required for hermeneutical inquiry with children and outline specific methods that can be used, oriented by guiding questions. This hermeneutical methodology can help advance our understanding of children’s experiences as well as their aspirations and concerns in research and in professional practice.
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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 | 0.062 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.012 | 0.120 |
| Scholarly communication | 0.019 | 0.024 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".