Comparing dominance hierarchy methods using a data-splitting approach with real-world data
Bibliographic record
Abstract
Abstract The development of numerical methods for inferring social ranks has resulted in an overwhelming array of options to choose from. Previous work has established the validity of these methods through the use of simulated datasets, by determining whether a given ranking method can accurately reproduce the dominance hierarchy known to exist in the data. Here, we offer a complementary approach that assesses the reliability of calculated dominance hierarchies by asking whether the calculated rank order produced by a given method accurately predicts the outcome of a subsequent contest between two opponents. Our method uses a data-splitting “training–testing” approach, and we demonstrate its application to real-world data from wild vervet monkeys (Chlorocebus pygerythrus) collected over 3 years. We assessed the reliability of seven methods plus six analytical variants. In our study system, all 13 methods tested performed well at predicting future aggressive outcomes, despite some differences in the inferred rank order produced. When we split the dataset with a 6-month training period and a variable testing dataset, all methods predicted aggressive outcomes correctly for the subsequent 10 months. Beyond this 10-month cut-off, the reliability of predictions decreased, reflecting shifts in the demographic composition of the group. We also demonstrate how a data-splitting approach provides researchers not only with a means of determining the most reliable method for their dataset but also allows them to assess how rank reliability changes among age–sex classes in a social group, and so tailor their choice of method to the specific attributes of their study system.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".