Three methodological approaches to studying singlehood
Bibliographic record
Abstract
Abstract As the number of single (unpartnered) individuals continues to rise, researchers across various disciplines have started to pay more attention to single individuals' lives. Yet, compared to the accumulated knowledge about experiences within romantic relationships, there is far less known about various experiences within singlehood. For singlehood research to grow in both quantity and quality, it is essential that research findings are critically evaluated both in terms of robustness of the evidence and validity of the inferences. In this paper, we review three broad approaches researchers have taken to understand singlehood that centered on (a) between‐group status (i.e., single vs. in a relationship) differences, (b) within‐person status differences, and (c) within‐group variability among singles. With a focus on well‐being as an outcome, we illustrate how each approach provides unique insights into singlehood and what caveats there are in interpreting results derived from each approach. Finally, we identify questions or methods that have not been extensively explored within each approach and offer suggestions for future research directions.
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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 | 0.187 | 0.252 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.009 | 0.022 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.007 | 0.017 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".