Working across religions, cultures, settings, and development: Protocol for wave 2 data collection with children and parents by the developing belief network
Bibliographic record
Abstract
The Developing Belief Network is a global research collaborative studying religious development in diverse social-cultural settings, with a focus on the intersection of cognitive mechanisms and cultural beliefs and practices in early and middle childhood. The current manuscript describes the study protocol for the network's second wave of data collection, which aims to further explore the development and diversity of religious cognition and behavior using a multi-time point approach. This protocol is designed to investigate three key research questions-how children represent and reason about religious and supernatural agents, how children represent and reason about religion as an aspect of social identity, and how religious and supernatural beliefs are transmitted within and between generations-via a set of eight tasks for children between the ages of 5 and 13 years and a survey completed by their parents/caregivers. This study is being conducted in 41 distinct cultural-religious settings, spanning 16 countries and 12 written languages. In this manuscript, we provide detailed descriptions of all elements of this study protocol, and give a brief overview of the ways in which this protocol has been adapted for use in diverse religious communities. As one example of how this protocol has been implemented outside of the United States, we present Arabic- and English-language study materials for children being raised in one of the following religious traditions in Lebanon: the Druze faith, Maronite Christianity, Orthodox Christianity, Shia Islam, or Sunni Islam. We end with reflections on the challenges of developing and implementing large-scale, multi-site, multi-time point studies of child development; our approach to navigating these challenges; and our suggestions for how future researchers might learn from our experiences and build on the work presented here.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".