European political trends viewed through patterns of Web linking
Bibliographic record
Abstract
Abstract This study explored the feasibility of using Web hyperlink data to study European political Web sites. Ninety‐six European Union (EU) political parties belonging to a wide range of ideological, historical, and linguistic backgrounds were included in the study. Various types of data on Web links to party Web sites were collected. The Web colink data were visualized using multidimensional scaling (MDS), while the inlink data were analyzed with a 2‐way analysis of variance test. The results showed that Web hyperlink data did reflect some political patterns in the EU. The MDS maps showed clusters of political parties along ideological, historical, linguistic, and social lines. Statistical analysis based on inlink counts further confirmed that there was a significant difference along the line of the political history of a country, such that left‐wing parties in the former communist countries received considerably fewer inlinks to their Web sites than left‐wing parties in countries without a history of communism did. The study demonstrated the possibility of using Web hyperlink data to gain insights into political situations in the EU. This suggests the richness of Web hyperlink data and its potential in studying social‐political phenomena.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".