The problem of unmeasured variables in animal social network analysis: can edge-based multilevel models provide a reliable solution?
Bibliographic record
Abstract
Abstract Recent approaches to analysing social networks suggest that modeling the edges of the network and using multilevel models will produce more informative estimates. These recent methods have been proposed as a way to better handle the dependency structures of social networks, account for biases in data collection, and retain uncertainty when making inferences about social network structures. We find that they have the potential to also effectively handle unmeasured variables that act as statistical confounds in social network analysis. Using simulated data, we highlight that static social network analyses can be used to identify patterns in social networks, but generally cannot be used to identify the underlying mechanisms behind the patterns. To identify mechanisms, we show that taking a dynamic approach and using edge-based models with additive and multiplicative random effects provides a means to identify mechanisms even when statistical confounds are present. Additive and multiplicative random effects also provide information about social structures not captured by the predictors, facilitating exploratory analysis. We suggest that a keep-it-maximal approach for random effects structures is beneficial for edge-based multilevel models of social networks, and that such approaches can be particularly effective when there are unmeasured variables that are not captured by model predictors.
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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 | 0.035 | 0.172 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".