The cohesion of National and Cultural networks during periods of stress
Bibliographic record
Abstract
Human beings tend to organize themselves in groups. These groups need to be robust to enable effective cooperation among individuals. According to some researchers (Ostrom, 1990; Suárez et al., 2011), a collective group identity based on shared cultural symbols, a shared religion or a common language is key to foster cooperation. To investigate this hypothesis, data was extracted from Twitter and two network graphs (the nodes were Twitter users and the links were the relationships among users) were created around two Spanish political parties during the 2017 Catalan elections, Ciudadanos and Podemos. On the one hand, the members of Ciudadanos’ network shared ideological positioning and cultural collective identity (they identified themselves with Spanish cultural symbols). On the other hand, Podemos’ members in the network shared ideological positioning but not a cultural identity (some of Podemos’ users identified with Catalan symbols and others with Spanish symbols). The results of different network cohesion metrics (e.g., Clustering Coefficient and Average Distance) show that Ciudadanos’ network was more cohesive than Podemos’ one.
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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".