Bibliographic record
Abstract
Agonistic behavior features prominently in hypotheses that explain how social variation relates to ecological factors and phylogenetic constraints. Dominance systems vary along axes of despotism, tolerance, and nepotism, and comparative studies examine cross-species patterns in these classifications. To contribute to such studies, we present a comprehensive picture of agonistic behavior and dominance relationships in wild female blue monkeys (Cercopithecus mitis), an arboreal guenon, with data from 9 groups spanning 18 years. We assessed where blue monkeys fall along despotic, tolerant, and nepotistic spectra, how their dominance system compares to other primates, primarily cercopithecines, and whether their agonistic behavior matches socioecological model predictions. Blue monkeys showed low rates of mainly low-intensity agonism and little counter-aggression. Rates increased with rank and group size. Dominance asymmetry varied at different organizational levels, being more pronounced at the level of interactions than dyad or group. Hierarchies were quite stable, had moderate-to-high linearity and directional consistency and moderate steepness. There was clear maternal rank inheritance, but inconsistent adherence to Kawamura's rules. There was little between-group variation, although hierarchy metrics showed considerable variation across group-years. Overall, blue monkeys have moderately despotic, moderately tolerant, and nepotistic dominance hierarchies. They resemble other cercopithecines in having significantly linear and steep hierarchies with a generally stable, matriline-based structure, suggesting a phylogenetic basis to this aspect of their social system. Blue monkeys most closely match Sterck et al.'s [1997] Resident-Nepotistic-Tolerant dominance category, although they do not fully conform to predictions of any one socioecological model. Our results suggest that socioecological models might better predict variation within than across clades, thereby incorporating both ecological variables and phylogenetic constraints. Our findings also highlight the need for clearer definitions of socioecologically relevant dominance categories, which would ideally derive from quantitative measures of dominance behavior. Intraspecific and methodological variation may, however, be a challenge.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".