Network reaction norms: taking into account network position and network plasticity in response to environmental change
Bibliographic record
Abstract
Abstract Recent studies have highlighted the link between consistent inter-individual differences in behaviour and consistency in social network position. There is also evidence that network structures can show temporal dynamics, suggesting that consistency in social network position across time does not preclude some form of plasticity in response to environmental variation. To better consider variation in network position and plasticity simultaneously we introduce the network reaction norm (NRN) approach. As an illustrative example, we used behavioural data on chacma baboons, collected over a period of seven years, to construct a time series of networks, using a moving window. Applying an NRN approach with these data, we found that most of the variation in network centrality could be explained by inter-individual differences in mean centrality. There was also evidence, however, for individual differences in network plasticity. These differences suggest that environmental conditions may influence which individuals are most central, i.e., they lead to an individual x environment interaction. We suggest that expanding from measures of repeatability in social networks to network reaction norms can provide a more temporally nuanced way to investigate social phenotypes within groups, and lead to a better understanding of the development and maintenance of individual variation in social behaviour.
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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 | 0.003 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".