Clues About Conducting Research With Children: Microethical Moments in an Interviewing Experience
Bibliographic record
Abstract
This article reflects on the process of constructing and conducting interviews with children, emphasizing the microethical moments that arise, explored through two theoretical-methodological “clues.” It examines the ethical challenges and tensions inherent in research shaped by adult-centric and developmentalist logics. We present strategies to address these limitations, including recognizing children as active subjects, using a registry of informed agreement in video format, engaging in joint negotiation, employing chat-interviews, and encouraging the use of drawing. Through an “epistemological vigilance,” the study advocates for a balance between protecting children and ensuring their meaningful participation, contributing to ethical practices in research involving 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 | 0.056 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.066 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.002 | 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".