Gauging node consistency in accusation–endorsement networks
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 Many signed, directed social networks can be viewed as being composed of positive (endorsements) and negative (accusations) directed edges, and these networks can in turn be created through a variety of different processes. The recently proposed consistency dynamics supposes that when nodes expect to be judged based on their associations in the network, they may create edges out of a desire to appear as having consistent judgements. We develop a quantifiable score that can rate the level of consistency in a node’s judgement. We demonstrate that this consistency score can be efficiently estimated using a modification of the popular personalized PageRank algorithm and evaluate the score’s properties. In order to validate this score’s relevance to empirical networks, we use consistency scores to perform an edge prediction task, and demonstrate that it performs competitively with, and adds complementary information to, more complicated measures designed specifically for that task. We also demonstrate that the nodes in these networks exhibit specific behaviours that consistency can identify across a range of parameterization values and which are not recoverable by other measures in isolation.
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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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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