FAMILY PROFESSIONALS’ ATTITUDES AND STANCE-TAKING ON POST-DIVORCE FATHERHOOD: A QUALITATIVE ATTITUDE APPROACH
Bibliographic record
Abstract
This article examines divorce professionals’ attitudes and stances in response to common criticisms of how they deal with divorce outcomes for fathers, according to which men are discriminated against in negotiations on the custody and living arrangements of their children. The study applied the relatively new qualitative attitude approach, and hence a further aim was to test its fitness for studying attitudes. Eighteen Finnish family professionals who worked with divorce cases — social workers, psychologists, district court judges, and lawyers — participated in semi-structured interviews in which they discussed claims designed to be provocative. The family professionals were found to show both collective, shared attitudes and diversity in attitudes and stances. The participants strove to position themselves as gender-neutral and as promoters of equality between mothers and fathers, and thus in accordance with the ideal of a good professional. The divorce professionals argued that their overriding aim was to secure the well-being of children. The method revealed some attribution bias, manifested as victim blaming, where fathers themselves were in part held accountable for the gendered post-divorce situation. The results highlight potential areas of cooperation between different types of divorce professionals that could lay a foundation for improving services and support for divorced parents and 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 | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".