Bibliographic record
Abstract
At the end of the Second World War, the city of Berlin was located 100 miles (160 km) inside the Soviet Occupation Zone of Germany. The Western Allies insisted on keeping part of the city for themselves, and so it was divided into four sectors, mimicking the rest of Germany. Stalin needed to persuade the British, French and Americans to leave so that there would be nothing in the way of him completing the strategic buffer of territory reaching from the Baltic Sea to the Adriatic, which Churchill would later christen the ‘Iron Curtain’. Cold War Berlin: An Island City is the story of how Stalin imposed his iron will over eastern Germany, and how he tried to squeeze his former allies out by cutting off their lines of supply and blockading the city. It examines the logistical miracle of the Berlin Airlift, which fed and heated a city of over two million people for almost eleven months. It is a story of alliances forged in the uncertainty of conflict, based on common interests and pragmatic convenience, alliances that would shape the twentieth century but would be betrayed for strategic or political reasons. It is also the tale of how competing ideologies came face to face in the city of Berlin and the new 'Cold War' that would come to dominate the second half of the 20th century was created out of the embers of the Second World War. The book is richly illustrated with photos, numerous maps and colour profiles and is the first in a mini-series by this author for Helion’s Europe@War series on Cold War Berlin.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.046 | 0.011 |
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".