The Role of Memory and Cognition (vs. Activity and Behavior) in Social Networks
Bibliographic record
Abstract
Social network research is increasingly recognizing that what happens in the minds of network actors their memories and cognitions can matter as much, if not more so, than more visible activities or behaviors. The purpose of this symposium is to highlight new research studies and perspectives that go beyond the traditional view of networks in action, which have focused on interactions among network actors and the structure of these interactions. Instead, we aim to examine how actors think about their ties and the surrounding networks especially over time and the impact of these memories and cognitions on network outcomes. Why Do High-Status People Have Bigger Networks? Status-Quality Coupling Drives Networking and Size Presenter: Jiyin Cao; Stony Brook U.-State U. of New York Presenter: Edward Bishop Smith; Northwestern Kellogg School of Management Hiding Knowledge Sharing Relationships from Rivals Presenter: You-Ta Chuang; York U. Presenter: Fu-Sheng Tsai; Cheng Shiu U. Presenter: Wenpin Tsai; Pennsylvania State U. Presenter: Martin J. Kilduff; UCL School of Management Toward a Theory of Gestalt vs. Elemental Network Perception Presenter: Tiziana Casciaro; U. of Toronto Dormant Ties: A Review and Agenda for Research Presenter: Jason Rekus Ross; U. of Kentucky Presenter: Ajay Mehra; U. of Kentucky Presenter: Daniel Z. Levin; Rutgers U. Presenter: Jorge Walter; George Washington U. Is Tie Maintenance Really Necessary? Presenter: Daniel Z. Levin; Rutgers U. Presenter: Jorge Walter; George Washington U.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".