Oppositions, joints, and targets: the attractors that are the glue of social interactions
Bibliographic record
Abstract
Social interactions are often analyzed by scoring segments of predefined behavior and then statistically assessing numerical and sequential patterns to identify the structure of the encounters. However, this approach can miss the dynamics of the animals' relationship over the course of the encounter, one that often involves invariant bonds, say a nose-to-nose orientation, with many different movements performed by both partners acting to counteract each other's attempts to break or maintain the relationship. Moreover, these invariant bonds can switch from one configuration to another during an interaction, leading from one stable configuration to another. It is this stepwise sequence of configurational stabilities that lead to functional outcomes, such as mating, aggression, or predation. By focusing on the sequence of invariant relational configurations, the deep structure of interactions can be discerned. This deep structure can then be used to differentiate between compensatory movements, no matter how seemingly stereotyped they may appear, from movement patterns which are restricted to a particular form when more than one option is available. A dynamic perspective requires suitable tools for analysis, and such tools are highlighted as needed in describing particular interactions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".