“Social cohesion analysis of networks: a novel method for identifying cohesive subgroups in social hypertext” by Alvin Chin, with Jessica Rubart as coordinator
Bibliographic record
Abstract
Alvin Chin is a Member of Research Staff at Nokia Research Center (NRC), Beijing, working in the Mobile Social Networking group. Alvin recently completed his PhD in Computer Science from the University of Toronto where he worked under Professor Mark Chignell. His PhD thesis was entitled "Social Cohesion Analysis of Networks: A Novel Method for Identifying Cohesive Subgroups in Social Hypertext" where he created a framework for automatically identifying influential members in subgroups from online social networks. At NRC Beijing, Alvin's research involves creating novel solutions that use the cell phone and context to participate and integrate with other users and online social networks, and enabling an intuitive user experience for social networking. He graduated with a Bachelors degree in Computer Engineering and a Masters degree in Electrical and Computer Engineering from the University of Waterloo. He has worked 2.5 years full time in industry researching emerging technologies in the wireless and pervasive computing area, especially Bluetooth and 802.11. His current research interests include social networking, computer-supported collaborative work, context-aware computing, and pervasive computing. Alvin is an active user of social networking and Web 2.0 technologies. He can be contacted at alvin.chin@nokia.com, and blogs frequently at http://www.alvinychin.com/blog.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".