Parents' approaches to conversations with their 5– to 18‐year‐olds about the 2024 US presidential election
Bibliographic record
Abstract
Abstract Parents serve as primary agents of political socialization for their children. The present study examined how parents in the United States engaged in conversations with their children (5–18 years) about the 2024 U.S. presidential election. Using a nationally diverse sample of 1001 parents (reporting on 1769 children), we investigated the occurrence, frequency, and approach taken toward these discussions, and the factors that predicted them. The majority of parents (84%, n = 843) reported speaking to at least one of their children, of whom 65% ( n = 543) spoke to all of their children. Whether and how often the conversations occurred varied by several demographic factors (e.g., child age and gender, parent gender and education, and family size), political interest, child anxiety about the election, and communication approach. Notably, with a more active and less avoidant communication approach, parents were significantly more likely to talk to their children about the presidential election, and with a more active approach the frequency of conversations increased. Given the importance of conversational approaches in the occurrence and frequency of such conversations, predictors of parents’ approach were explored. Together these findings contribute to a growing understanding of the mechanisms that drive parents’ political socialization of their children.
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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 | 0.001 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".