Self-Regulation as Represented by Three Albertan Parenting Magazines
Bibliographic record
Abstract
Introduction Self-regulation (SR) is the ability to alter behavioral responses in response to interactions with the environment. Children who develop skills associated with high SR early in life are more prepared for school than children who fail to develop these skills. Methods An environmental scan was used to collect data from 3 Albertan parenting magazines: Apple Magazine, Calgary Child’s Magazine, and Red Deer Child’s Magazine published within January 2010- January 2015. A textual analysis was conducted to identify the overall SR messages presented to the public. Inductive coding was used to organize similar themes into larger categories and thereafter to determine how these Albertan media resources suggest the development of SR in children. Results Three themes emerged from the scan: parental role in the development of SR, disciplining children in way that builds SR, and coping mechanisms for parents in response to tantrums. Discussion Albertan media resources describe SR in a way that is consistent with the current literature. Public resources available to parents address issues related to SR and effective management tools for parents. However, long-term consequences of early development of SR are not discussed as frequently. These results will be used to communicate with policy makers in order to develop or strengthen interventions targeted toward children who are at risk for developing poor SR.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".