A roadmap to doing culturally grounded developmental science
Bibliographic record
Abstract
This paper provides a roadmap for engaging in cross-cultural, developmental research in practical, ethical, and community-engaged ways. To cultivate the flexibility necessary for conducting cross-cultural research, we structure our roadmap as a series of questions that each research program might consider prior to embarking on cross-cultural examinations in developmental science. Within each topic, we focus on the challenges and opportunities inherent to different types of study designs, fieldwork, and collaborations because our collective experience in conducting research in multiple cultural contexts has taught us that there can be no single “best practice”. Here we identify the challenges that are unique to cross-cultural research as well as present a series of recommendations and guidelines. We also bring to the forefront ethical considerations which are rarely encountered in the laboratory context, but which researchers face daily while conducting research in a cultural context which one is not a member. As each research context requires unique solutions to these recurring challenges, we urge researchers to use this set of questions as a starting point, and to expand and tailor the questions and potential solutions with community members to support their own research design or cultural context. This will allow us to move the field towards more inclusive and ethical research practices.
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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 | 0.222 | 0.157 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.013 | 0.057 |
| Scholarly communication | 0.026 | 0.040 |
| Open science | 0.008 | 0.037 |
| Research integrity | 0.014 | 0.031 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".