Korean Fathers’ Value of Children and Their Patterns of Parenting Behavior: A Cluster Analysis
Bibliographic record
Abstract
Studies concerning fathering have rarely examined the relationships between how fathers value their children and their parenting behaviors. The purpose of this study was to identify Korean fathers’ value of children and patterns of parenting behavior through cluster analysis. Data from 1,520 fathers who participated in the 2013 Panel Study on Korean Children (PSKC) were subjected to a two-step cluster analysis using the k-means algorithm for clustering fathers’ value of children and their parenting behaviors. The results indicated three clusters: “only valued emotionally,” “highly involved,” and “detached.” Furthermore, when the influencing factors dividing clusters 2 and 3 were explored, the fathers’ psychological characteristics, such as high parenting stress, depression and low self-esteem, in addition to their sociodemographic factors, increased the likelihood of being assigned to the “detached” cluster. The significance of this study lies in the clustering of fathers using their value of children and parenting behaviors, which is an approach that had not been studied previously. Moreover, this study is valuable as it identifies the relative influence of fathers’ psychological characteristics that affected the classification of clusters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".