When couples fight about money, what do they fight about?
Bibliographic record
Abstract
Conflicts about money and finances can be destructive for both the quality and longevity of relationships. This paper reports on a descriptive analysis of the contents of financial conflicts in two samples. Study 1 examined severe financial conflicts in social media posts (N = 1014) from reddit (r/relationships). Eight themes were identified via thematic analysis: “unfair relative contributions” “who pays for joint expenses”, “job and income”, “exceptional expenses”, “terms of financial arrangements”, “discrepant financial values”, “one-sided financial decisions”, and “perceived irresponsibility”. Study 2 examined reports of more mundane financial disagreements recalled by married individuals (N = 481). Seven themes were identified via thematic analysis: “relative contributions”, “job and income”, “different values”, “exceptional expenses”, “mundane expenses”, “money management”, and “perceived irresponsibility”. In both samples, themes could be ordered along the dimensions of “concerns about fairness” and “concerns about responsibility”. The association of relationship outcomes (perceived partner responsiveness, couple satisfaction) with each theme and demographic predictors (income, relationship length, shared finances) were explored. Independent t-tests suggested that participants who recalled disagreements fitting the themes at the extreme ends of the two dimensions (“unfair relative contributions” and “perceived irresponsibility”) reported worse relationship outcomes. In contrast, participants recalling disagreements fitting the theme of “mundane expenses” reported better relationship outcomes.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| 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".