Selling Berlin: Imagebildung und Stadtmarketing von der preussischen Residenz bis zur Bundeshauptstadt
Bibliographic record
Abstract
This collection of essays on the history of Berlin's Stadtmarketing developed out of a 2005 interdisciplinary workshop organized by the editors to test whether the competitive drive to create marketable images for cities has deep historical roots or whether it is a recent response to globalization. The twenty-one authors included identify a long tradition of marketing Berlin, which consistently necessitated negotiation between competing and often acrimonious interests within the city. Anyone looking for reference to the economic side of the ‘sale’ will be disappointed. The editors are explicit in their intention to wrestle the theme of marketing away from the economic and business historians. Instead, the focus is on three categories of actors who have contributed to Berlin's image-making: first, officials, from Prussian kings to local politicians and occupying forces; second, citizen or corporate interests united with political authorities to present a positive image of the city; and third, the critical or oppositional voices who try to interject their own alternative visions (p. 15).
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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 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".