Mis-Information Removal in Social Networks: Constrained Estimation on Dynamic Directed Acyclic Graphs
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Bibliographic record
Abstract
A key issue in the multi agent state estimation presented in social networks is the inadvertent multiple re-use of data also known as mis-information propagation or data incest. We formulate this mis-information propagation in a graph theoretic setting and give a necessary and sufficient conditions on the topology of information flow network so that the underlying state can be estimated optimally. A distributed fusion algorithm is proposed so that the social network has incest free estimates. We also provide a discussion on mis-information removal algorithm for information exchange protocols where people learn from actions of others in a social network. A sub-optimal algorithm is also presented when the information flow graph is not known. Numerical examples are provided to illustrate the performance of the proposed optimal and sub-optimal algorithms.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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