The relation between children’s task‐specific competence and mothers’ controlling practices
Bibliographic record
Abstract
Abstract In the guided learning domain of socialization, studies examining the antecedents of controlling parenting suggest that children’s lack of competence in a task could trigger controlling practices in that task. However, a stringent test of this relation remains to be conducted. This study examined this relation using a sample of 101 children (Mage = 10.21 years) and their mothers, a standardized measure of children’s competence in a task that was unfamiliar to the participants, and multi‐informant observational measures of maternal controlling practices during a mother–child interaction involving that task (rated by an independent coder and the children). Path analyses showed that children’s initial lack of competence in a task was related to higher levels of coded maternal controlling practices during a subsequent mother–child interaction involving that task, which in turn were positively linked to children’s perceptions of their mothers’ practices as controlling. A bootstrap analysis also confirmed that the indirect link from children’s competence to perceived maternal controlling practices through coded maternal controlling practices was significant. These effects were observed while controlling for mothers’ self‐reported controlling parenting style and perceptions of their children’s academic skills. Implications of these findings for the promotion of optimal parenting and future research directions are discussed.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".