Don't be a Deadbeat Dad: Non-Custodial Fathers, Stereotypes, and Family
Bibliographic record
Abstract
The objective of this paper is to explore how gender stereotypes influence social structures which creates barriers for non-custodial fathers (NCF) and to identify possible solutions to challenge societal prejudice. The findings suggested that gender stereotypes shape NCF’s perception of fatherhood, the mother’s opinion of NCFs, the proceedings during a custody battle, and the type of social services provided to NCFs. These act as barriers that negatively affect NCF-child relationships, which are further diminished by non-cooperative mother-father relationships. Surprisingly, stepfamilies were found to improve NCF-child relationships. Remote fathering was found to be detrimental to children’s development that can result in poor socioemotional adjustment, increased externalized problems, diminished abilities to form strong bonds with friends, and feeling lonely. It was also found that absent fathers increased the risk of their children developing behavioural problems, engaging in criminal behaviour, and experience poverty. Based on qualitative data, possible solutions outreach programs created through community collaboration, that promote education, empathy and respect. For more problematic NCF visitation, perhaps a version of the Israeli visitation center could be used. In sum, these conceptual solutions can challenge gender stereotypes and social prejudice which benefits NCFs and family as a social institution.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".