Going Beyond Adaptation: An Integrative Review and Ethical Considerations of Semi-Structured Interviews With Elementary-Aged Children
Bibliographic record
Abstract
There are currently no methodological frameworks available to conduct semi-structured interviews in the education and social sciences fields with children of primary school age, between 6–12, having no history of trauma or disabilities. To fill this gap, we conducted an integrative review of the literature to uncover current methodological considerations using the PRISMA procedure to search the ERIC, SAGE, ProQuest CBCA, SciELO and Redined databases. 19 methodological articles across multiple disciplines were retrieved and analyzed through inductive content analysis. We found that using traditional semi-structured interviews with children brings inherent challenges and that adaptation of current methods is not sufficient. A preliminary set of interviewing guidelines emphasizing a flexible and laissez-faire approach as well as a list of alternatives to the semi-structured interview for elementary-aged children are proposed, laying the foundations for a much-needed overhaul of current interviewing methods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".