Sociality and Elementary Forms of Structure
Bibliographic record
Abstract
By looking at networks as collections of smaller elementary structural forms – mainly all combinations of two nodes (dyads) and three nodes (triads) among whom ties may or may not exist – one can learn much about the larger structure. This is especially useful when that structure is very large and therefore difficult to see as a whole. And yet, these most elementary forms of social structure are not simply mathematical constructs; they reflect the fundamental ways that social actors relate with one another as individuals and as social units (i.e., sociality). Thus, a network with many social elements of one type, and fewer of another, suggests a certain way of relating involved in how the network has formed and where it might be going. In this chapter, we introduce the reader to dyads and triads as forms of interacting and relating. We cover techniques for decomposing networks into these constituent elements and connecting variation at the micro level as a way of seeing macro-level structures.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".