Understanding the development of honesty in children through the <scp>domains‐of‐socialization</scp> approach
Bibliographic record
Abstract
Abstract Honesty is an important value that children acquire through socialization. To date, the socialization process by which children learn to behave honestly remains relatively unexamined. Researchers may have left this area of research relatively unexamined because there is no framework to understand how parents socialize honesty and lie‐telling in their children. As such, we suggest that the domains‐of‐socialization approach, which organizes the socialization process into various domains based on different aspects of the caregiver‐child relationship, may provide such a framework. Using this framework, researchers can operationalize vague parenting variables and identify gaps in the research, allowing them to investigate the relationship between socialization and developmental trajectories of honesty and lie‐telling tendencies more thoroughly. In this paper, we review the literature on factors influencing children's lie‐telling and honesty in relation to the five domains to demonstrate the applicability of the domains‐of‐socialization framework to research on the socialization of honesty. We also provide recommendations for future research on the socialization of honesty using a domain‐specific approach, which will contribute to our understanding of how children develop into normative or problematic liars.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| 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 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".