Bibliographic record
Abstract
Despite a succession of scholarly studies over the years, the relationship between Reza Shah’s Iran and National Socialist Germany has not been fully explored. Rather than focusing on the supposed Aryan ideological sympathies that bound the two countries together, this article argues that the real driver of the German–Iranian relationship in the 1930s was economic and based in the mutual interaction of state economic initiatives. It states that Iran’s place in Nazism’s economic system was the outcome of two factors: the “New Plan” of Reich Economics Minister Hjalmar Schacht, and its focus on clearing agreements as a motor for depression-era trade, and the connections of Schacht’s system to Reza Shah’s strategy to modernize Iran. In exploring this issue the article focuses on relations between Germany and Iran during three distinct moments: first, the period from 1918 to 1928 and the working out of a new relationship after the First World War; secondly the period of Schacht’s New Plan in Iran in the mid-1930s; and finally the period from the signing of the Nazi-Soviet Pact in 1939 to the British–Soviet invasion of Iran in 1941. During this last period Iran both belonged to the Nazi–Soviet trade zone created by the Pact and attempted to defend its neutrality.
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 | 0.001 | 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 | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".