How gender, age, and socioeconomic status predict parenting goal pursuit
Bibliographic record
Abstract
There are many factors that may influence parenting, from societal norms and expectations, dispositional differences, experience and maturity, and availability of resources. In the current research, we examined how stable demographic characteristics associated with these different factors predict the goals parents pursue with their children. We examined whether the pursuit of four parenting goals—child love and security, child development, parent image, and child acceptance—varies based on the characteristics of parents (i.e., gender, age, and socioeconomic status) and their children (i.e., gender and age). First, we provided evidence for the measurement invariance of the Parenting Goals Scale. The results suggested that across key characteristics, parents largely pursue the same four parenting goals on which they could be meaningfully compared. Second, meta-analytic results (<i>k</i> = 5; <i>N</i> <sub>total</sub> = 2,240) indicated that parents were largely similar in the goals they pursued with their children across their own and their child’s characteristics. We identified only a few exceptions, with these differences being small in magnitude: mothers and noncollege-educated parents pursued child love and security goals more than fathers and college-educated parents, older parents pursued child development goals less than younger parents, parents of older children pursued image goals more than parents of younger children, and lower income parents pursued child acceptance goals more than higher income parents. These results suggest that while there may be some small differences in parenting goal pursuit based on demographic characteristics, parents are largely motivated by similar goals when caring for 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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".