Relational Attribute Integrated Matching Analysis (RAIMA): A Framework for the Design of Self-Adaptive Egocentric Social Networks
Why this work is in the frame
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Bibliographic record
Abstract
An emerging research in pervasive computing is the inference of social context to facilitate and mediate communications among proximate people. Understanding users' needs through information reasoning and leveraging principles of social networks play an important role in the emergence of innovative computer-mediated social networks. This paper introduces a generic social networking framework for the analysis and visualization of mobile and spontaneous social networks. The proposed framework is capable of analyzing social scores in order to provide decision support to users in the form of egocentric social graphs. As part of the framework, we introduce a matching algorithm that its efficiency is compared to commonly used “Stable Marriage Matching” algorithms in opportunistic social networks. We show the performance of the algorithm as social profile attributes increase in a network.
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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 | 0.003 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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