Generalized affiliation indices extract affiliations from social network data
Bibliographic record
Abstract
Summary In the analysis of animal social networks, a common challenge has been distinguishing affiliations – active preferences of pairs of individuals to interact or associate with one another – from other, structural, causes of association or interaction. Such structural factors can include patterns of use of the habitat in time and space, gregariousness and differential association rates among age/sex classes. In an approach with similarities to the multiple regression quadratic assignment procedures test, we suggest calculating generalized affiliation indices as the residuals from a regression of the measures of association or interaction on structural predictor variables, such as gregariousness and spatiotemporal overlap. If the original data are association indices or counts of interactions, then generalized linear models with binomial or Poisson error structures, respectively, can be used in place of linear regression. Anscombe or deviance residuals can be used to assess the significance of particular affiliation indices. Generalized affiliation indices can be used as the weights of links in a social network representation. They can then be portrayed in network diagrams or cluster diagrams and used to calculate network statistics, to delineate communities by maximizing modularity and to test for overall affiliation using data‐stream permutation tests. We evaluate the effectiveness of such generalized affiliation indices using simulated and real association data, finding that the method removes much of the effect of structural variables on association patterns, revealing real affiliations. While the approach is very promising, it is limited by the extent to which the input predictor variables represent important structural factors.
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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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".