Ivan Turgenev als Netzwerker: Digitale Kuratierung seiner europäischen, insbesondere deutschen literarischen Kontakte (am Beispiel seines Briefwechsels vom Juni 1868 bis Mai 1869)
Bibliographic record
Abstract
The Russian writer Ivan Sergeevič Turgenev (1818–83), who lived in Western Europe (Germany, England, and France) during the second half of his life, is considered the most important mediator between Russia and Europe in the nineteenth century due to his wide and intensive contacts in East and West. The paper aims to trace Turgenev’s literary and cultural contacts using the epistemological model of the net and current methods of analyzing social networks on a quantitative and qualitative level. In concrete terms, Turgenev’s postal relations from a single year (from June 1868 to May 1869) are presented and evaluated in tabular form and as GEPHI graphs. Beyond the purely quantitative network visualization and viewing, the attempt is made to provide a cultural weighting of the exchange, especially of Turgenev’s German contacts. The network-specific weighting of these contacts results in a different emphasis than usual in Turgenev research, which focuses on Turgenev’s contacts with important German writers. The qualitative analysis carried out on the basis of the visualization shows that Turgenev’s contacts with literary celebrities such as Theodor Storm, Berthold Auerbach, and Paul Heyse proved to be weak ties. In contrast, his relationship with the little-known literary figure Ludwig Pietsch deserves to be called a strong tie. Turgenev’s position and agency in the network can be described with Burt as a “broker” attitude.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".