Father involvement among Chinese fathers in four geolocations: Exploring cultural nuances and similarities
Bibliographic record
Abstract
Abstract Objective The objective is to explore Chinese fathers' levels of involvement in their young children's lives in four geolocations: Canada, China, Hong Kong, and Taiwan. Background Fathering research has been primarily based on Westernized populations, with a dearth on Chinese fathering. Within the limited studies on Chinese fathers, the influence of sociopolitical environments and geolocation has been overlooked, decontextualizing father involvement. Method The study included 273 fathers across four geolocations: Canada ( n = 67), Mainland China ( n = 56), Hong Kong ( n = 47), and Taiwan ( n = 103) using time diary data (two 24‐hour accounts of their latest workday and weekend day). A series of repeated‐measures analyses of covariance (Father Involvement treated as the repeated measures; covarying Fathers' Age and Levels of Education) were conducted to explore the nuances and similarities of father involvement. Results Fathers' levels of engagement (play, care) differed. Fathers from China reported spending the most time playing with their child than did other fathers. Taiwanese and Hong Kong fathers spent similar amounts of time playing with and caring for their children. Other father involvement dimensions also differed by geolocation. Conclusion Our findings demonstrate that Chinese fathers cannot be “collapsed” into one group. Due to differing sociopolitical environments, fathers' involvement in various dimensions varied. Implications Chinese fathers are actively involved in their children's lives, contrary to the beliefs of fathers being “aloof and distant.” However, there are differences among Chinese fathers; thus, taking geolocation into account when providing programs and services is essential to Chinese communities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".