Geheime Kommunikationsräume? Die Staatssicherheit an der Friedrich-Schiller-Universität Jena
Bibliographic record
Abstract
For some time now, the trend in studies of East Germany’s Ministry for State Security, commonly known as the Stasi, has been away from grand narratives toward microhistories. The prolific Ilko-Sascha Kowalczuk, for one, has investigated select aspects of Stasi work, such as its telephone monitoring, or its role during the June 17 uprising of 1953, while other researchers associated with the Federal Commission for the Stasi Files have taken a territorial approach to East Germany’s main agent for repression, investigating the Stasi in several of its 217 district level offices (Kreisdienststellen, or KD). The Stasi’s influence at East German universities in East Berlin, Leipzig, Halle, Rostock and Greifswald has also been the subject of several monographs. Jens Gieseke’s comprehensive histories of the Stasi are an important exception here, but it is worth noting that his original survey, Mielke-Konzern, appeared more than 17 years ago. Katharina Lenski’s highly detailed account of the Stasi at the University of Jena therefore continues the trend of putting a particular aspect of the Stasi under a historical microscope. The result in this case is an astonishing revelation of the hundreds of ways that repression was built into the very structures of university bureaucracy. Although not as glamorous, and with only about half the student population of the better-known Humboldt University and the University of Leipzig, the University of Jena is nevertheless an appropriate subject of study, in large part because of its work with the Zeiss optics factory in town, which provided sighting and laser equipment to the German military.
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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 | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".