Goodness of fit indices of LCA models.
Bibliographic record
Abstract
<div> Research comparing monogamous and non-monogamous relationships on well-being indicators across diverse populations have yielded inconsistent findings. The present study investigates sociodemographic characteristics, as well as personal and relational outcomes, across different relationship configurations. Data were drawn from an online community-based sample of 1,528 LGBTQ+ persons aged 18 years and older in Quebec, Canada. A latent class analysis was performed based on legal relationship status, relationship agreement, cohabitation status, and the seeking of extradyadic sexual and romantic partners on the internet. Class differences on sociodemographic characteristics and well-being and relationship quality indicators were examined. A five-class solution best fit the data, highlighting five distinct relationship configurations: Formalized monogamy (59%), Free monogamy (20%), Formalized open relationship (11%), Monogamous considering alternatives (7%) and Free consensual non-monogamies (3%). Cisgender women were more likely to engage in monogamous relationships than cisgender men, who were overrepresented in open relationships. Lower levels of perceived partner support were observed in both free monogamous and consensually non-monogamous relationships, the latter of which also showed lower levels of well-being. Consensual non-monogamy researchers exploring relationship outcomes should examine relationship facets that go beyond relationship structure or agreement. Variations in monogamies and non-monogamies, both consensual and non-consensual, may be present within each broad relationship configuration, as reflected in different personal and relational needs, which can then translate to better or poorer outcomes. </div>
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.092 | 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; both teacher heads agree on what is shown here.
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".