Stuck in the middle with you: Predictors of commitment in midlife
Bibliographic record
Abstract
Rooted in commitment theory, this study examines dedication, perceived investments, and felt constraint, three unique aspects of commitment, in midlife using data gathered from a sample of 224 adults surveyed at ages 43 and 50 years. Positive and negative dimensions of couple relations (partner supportiveness and the extent to which one’s partner is demanding and critical, referred to as strain) assessed longitudinally and midlife-specific circumstances, including being in a first marriage or remarriage and parental and empty nest status, were examined as predictors of commitment at age 50. Latent change score modeling revealed partner support and strain as robust predictors of commitment. Higher levels of partner support at age 43 and a more gradual intraindividual decrease in support between ages 43 and 50 predicted higher dedication and perceived investments and less felt constraint at age 50. Higher age 43 levels of partner strain predicted more perceived investments and felt constraint at age 50, while a more rapid increase in strain predicted less dedication and more felt constraint. Being in a first or remarriage, being a parent, or having an empty nest did not predict commitment. These results highlight the importance of initial levels and changes in couple relations for shaping midlife commitment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".