How Do Researchers Question Children and Adolescents? A Systematic Assessment of Developmental Research Methods
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Both the kinds of exchanges and the context under which children are questioned may affect the quality of data. Yet, little is known about how developmental scientists communicate with children for research. Using manifest content analysis, the 3,119 manuscripts published in the top 20 developmental outlets in 2018 were coded for methodology, examining whether researchers communicated directly with children, how they did so, and how they contextualized questioning. We found that over 65% of empirical publications presenting new data questioned children. Researchers used a variety of methodologies (e.g., 64% questionnaires, 51% assessments, 5% interviews). As age increased, the odds of giving children standardized questionnaires, closed-ended questions, and Likert-type questions increased. Researchers rarely reported how they contextualized questioning and rarely utilized supplemental materials. Researchers should consider collecting more qualitative data, better reporting methodologies, and utilizing online spaces to share supplemental materials. These modifications can ensure that we produce the strongest data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it