A Current Approach to Logistic Regression Analysis of Birth Order and Sexual Orientation
Bibliographic record
Abstract
Numerous statistical procedures have been developed to examine the statistical relations between quantifiable aspects of an individual's sibship and the likelihood of that individual manifesting a homosexual preference. Our purpose in this methodological paper is explaining how to use and how to interpret the multiple regression approach introduced by Ablaza et al. (2022), modified by Blanchard (2022), and reorganized by Zdaniuk et al. (2025)-hereafter, the ABZ model. First, we list the sibship variables of present interest (e.g., number of older brothers), summarize their previously observed associations with sexual orientation, and discuss the language and labels that we recommend for describing empirical results in this research area. We then explain, in concrete, practical terms, how to analyze these sibship variables using the ABZ method, and we present a model analysis using previously published data. Our subsequent sections, which go more deeply into the topic, include a discussion of the mathematical-statistical rationale behind the Ablaza et al. approach-more specifically, its foundation on the ceteris paribus condition of multiple regression in conjunction with a "subsetting" property of the relevant sibship data. Finally, we compare the performance of the ABZ model with an older, more frequently used logistic regression model, and we discuss the potential application of the ABZ model to outcomes other than homosexuality.
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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 | 0.024 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".