Rethinking Collaboration: Medical Research and Working Relationships at the Iranian Pasteur Institute
Bibliographic record
Abstract
The Pasteur Institute of Iran underwent a major expansion of its research productivity and international recognition during some of the most significant events of modern Iranian history: the nationalization of the Iranian oil industry, followed by the Anglo-American coup against Prime Minister Mohammad Mosaddegh in 1953. During this period, the institute’s French director, Marcel Baltazard, was embedded in a complex set of working relationships with his Iranian employees, research subjects, and government ministers; American scientists and foreign aid workers; and French Pasteurians and diplomats. Baltazard constantly described these relationships as instances of “collaboration.” The temporal and geographical context demands a critical reading of scientific collaboration alongside the negative implications of political collaboration. Investigating the political commitments and social attitudes of the French director and Iranian staff, this essay demonstrates that scientific collaboration at the institute both reinforced socioeconomic inequalities within Iran and mirrored global Cold War geopolitics that undermined Iranian sovereignty.
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 | 0.025 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.029 | 0.042 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".