Understanding Sequencing in Social Network Communications
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
Comprendre le séquençage dans les communications dans un réseau social :Le séquençage est un processus décisionnel indispensable dans la circulation de l'information. Cette note de recherche propose la conceptualisation de séquençage pour comprendre comment et pourquoi les expéditeurs d'information « priorisent » certains membres du réseau pendant qu'ils communiquent avec d'autres. Nous examinons l'utilité de cette conceptualisation avec les données recueillies à partir de GRAND, un réseau académique. Le concept de séquençage permet aux chercheurs d'explorer les processus décisionnels qui surviennent avant le flux d'information et de lier le comportement des individus au contexte social. Sequencing is an indispensable decision-making process during information flows. This paper proposes the conceptualization of sequencing to understand how and why information senders prioritize some network members when they communicate with others. We examine the usefulness of this conceptualization with data collected from GRAND, a scholarly network. The concept of sequencing enables researchers to explore the decision-making process that occurs prior to information flows and link individuals’behavior to the social context.
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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 | 0.049 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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