The Mundane Politics of ‘Security Research:’ Tailoring Research Problems
Bibliographic record
Abstract
Since the late 20th century, Germany’s federal science policy has shifted towards an emphasis on commercialization and/or applicability of academic research. University researchers working within such strategic funding schemes then have to balance commitments to their government commission, their research, and their academic careers, which can often be at odds with each other. Drawing on an ethnographic study of the development of a ‘smart’ video surveillance system, I analyze some of the strategies which have helped a government-funded, transdisciplinary group of researchers to navigate conflicting expectations from their government, academia, and the wider public in their everyday work. To varying degrees, they managed to align conflicting expectations from the government and their departments by tailoring research problems which were able to travel across different social worlds. By drawing attention to work practices on the ground’, this article contributes ethnographic detail to the question of how researchers construct scientific problems under pressures to make their work relevant for societal and commercial purposes.
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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 | 0.286 | 0.269 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.031 | 0.182 |
| Scholarly communication | 0.041 | 0.045 |
| Open science | 0.005 | 0.026 |
| Research integrity | 0.015 | 0.016 |
| Insufficient payload (model declined to judge) | 0.003 | 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".