Identifying subcommunities using cohesive subgroups in social hypertext
Bibliographic record
Abstract
Web pages can be modeled as nodes in a social network, and hyperlinks between pages form links (relationships) between the nodes. Links may take the form of comments, for example on blogs, creating explicit connections between authors and readers. In this paper, we describe a novel methodology and framework for identifying subcommunities as cohesive subgroups of n-cliques and k-plexes within social hypertext. We apply our methodology to a group of computer technologists in Toronto called TorCamp who communicate using a Google group. K-plex analysis is then used to identify a group of people that forms a subcommunity within the larger community. The results are then validated against the experienced sense of community of people inside and outside the subcommunity. Statistically significant differences in experienced sense of community are found, with people within the subcommunity showing higher levels of perceived influence and emotional connection.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".