Overlooked Ties: An Examination of Neglected, Rare, or Special Relationships in Org Networks
Bibliographic record
Abstract
When one thinks of a network tie, a certain prototypical image comes to mind: a relationship where interaction is fairly frequent, active, positive, and largely routine. Most social network research examines such ties. Yet the literature has increasingly identified certain types of ties that do not necessarily fit this standard mold, such as dormant ties or negative ties, which may actually have an outsized influence beyond their numbers. Sometimes these overlooked ties are neglected and/or taken for granted but are actually much more common than can be explained by existing network theories or studies. Sometimes they are rare but provide insights into helping us to understand organizational relationships more generally. And sometimes they are special in terms of providing instrumental value or enhancing well-being beyond what is typical for most ties. This symposium is designed to bring to light these new and interesting types of relationships, with an eye towards integrating them conceptually into the field’s broader understanding of how social networks operate in practice. The Tie Over Time: Meaning, Memory, and Temporal Form Author: Jason Rekus Ross; U. of Kentucky Author: Ajay Mehra; U. of Kentucky Author: Daniel Z. Levin; Rutgers U. Author: Jorge Walter; George Washington U. Author: Stephen P. Borgatti; U. of Kentucky A Blessing in Disguise? Power, Imagined Ties, and Downstream Consequences Author: Velvetina Siu Ching Lim; UCL School of Management Author: Blaine Landis; U. College London Author: Clarissa Cortland; UCL School of Management Author: Robert Wilhelm Krause; Gatton College of Business and Economics, U. of Kentucky Maintaining Ties in Creative Work: The Separation Practices of Chef Protégés From Their Mentors Author: Daphne Ann Demetry; McGill U. Author: Rachel Doern; U. of London, Goldsmiths College What’s So Hard About Staying in Touch? Unpacking Tie Maintenance Author: Ye Jin Park; NYU Stern Author: Ko Kuwabara; INSEAD
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How this classification was reachedexpand
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 | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".