A review of two decades of <scp>LGBTQ‐inclusive</scp> research in <scp>JSPR</scp> and <scp>PR</scp>
Bibliographic record
Abstract
Abstract The field of lesbian, gay, bisexual, transgender, and queer (LGBTQ+) relationship science has grown significantly over the past two decades, coinciding with rapid changes in the social acceptance of LGBTQ+ people. However, it is unclear to what extent the top two journals in relationship science, the Journal of Social and Personal Relationships and Personal Relationships , have contributed to the field. In this critical review, we analyzed the 2181 manuscripts published in the journals between 2002 and 2021 for whether they included or excluded LGBTQ+ participants, the methodologies used to analyze their data, and their conclusions about LGBTQ+ lives and relationships. The overwhelming majority (85.8%) of manuscripts did not acknowledge LGBTQ+ relationships; however, there have been improvements compared to past research in retaining LGBTQ+ participants within a data set when they were present. We identified 92 manuscripts that contributed to knowledge about LGBTQ+ lives or relationships. We discuss the lack of intersectional analyses and methodological challenges of incorporating multiple forms of diversity within quantitative research. Overarching themes across manuscript content included minority stress, relationship formation, social support, and commitment. Overall, though the research in the two journals has contributed to the literature on LGBTQ+ relationships, our review suggests that scholars do not consider these two journals as a first choice for finding or publishing LGBTQ+ relationship science.
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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 | 0.036 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.026 | 0.021 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".