Identifying Fragments in Networks for Structural Balance and Tracking the Levels of Balance Over Time
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This paper presents three items. The first is a brief outline of structural balance oriented towards tracking the amount of balance (or imbalance) over time in signed networks. Often, the distribution of specific substructures within broader networks has great interest value. The second item is a brief outline of a procedure in Pajek for identifying fragments in networks. Identifying fragments (or patterns or motifs) in networks has general utility for social network analysis. The third item is the application of the notion of fragments to counting signed triples and signed 3-cycles in signed networks. Commands in Pajek are provided together with the use of Pajek project files for identifying fragments in general and signed fragments in particular. Our hope is that this will make an already available technique more widely recognized and used. Determining fragments need not be confined to signed networks although this was the primary application considered here.
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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 it