Between arts-based methods and hermeneutics: Navigating the ethics of participation in research with vulnerable children
Bibliographic record
Abstract
Children are underrepresented in research, mainly because of the ethical issues that their participation raises. This is even more true when it comes to marginalized migrant children. For the sake of justice, researchers must think about methods for including children and their families and reflect on the ethics of knowledge building in these contexts. This article returns to the hermeneutics of Hans Georg Gadamer as an interpretation practice which values understanding rather than explanation. It also focuses on the concept of play which can be truth revealing and which is understood as the very being of artistic practices. This allows us to first emphasize the compatibility of hermeneutics and art-based approaches, and their ethical value when it comes to bringing clinical and research issues together. This article highlights that methods based on art and play itself are ethical in the way that they can both provide well-being and reveal a truth that is as useful to the participant as it is to the researcher. This article also addresses how the researcher, in the process of understanding, is “at play” in the research situation which places him in an active role. The play allows a participatory truth to emerge, a truth resulting from a dialogue. This leads us to recognize the ethical value of participation in research with 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 | 0.187 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.019 | 0.229 |
| Scholarly communication | 0.030 | 0.021 |
| Open science | 0.003 | 0.029 |
| Research integrity | 0.008 | 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".