Romantic Relationships Conceptualized as a Judgment and Decision-Making Domain
Bibliographic record
Abstract
We review the emerging evidence suggesting that the largely separate research areas of romantic relationships and judgment and decision making (JDM) can usefully inform each other. First, we present evidence that decisions in more traditional JDM domains (e.g., consumerism, economics) share important features with romantic-relationship decisions, including the use of formal decision strategies (e.g., the investment model), intuitive shortcuts (e.g., the availability heuristic), and anticipated emotions (e.g., affective forecasting). In turn, we present evidence suggesting that incorporating key concepts from the field of relationships (e.g., need to belong, attachment style) can enrich traditional JDM domains. These largely unrecognized overlaps between relationship decisions and decisions made in more traditional decision-making domains suggest that the fields of relationship science and JDM—each of which contains a wealth of existing theory, findings, and research tools—could be used to illuminate one another for mutual benefit.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".