Bibliographic record
Abstract
An exploration of the link between politics of migration, prospects of integration, and ethnic identity among Iranian immigrants and their descendants in the United States, spanning from the 1970s to the present day. Thousands of Iranians fled their homeland when the 1978–1979 revolution ended the fifty-year reign of the Pahlavi dynasty. Some fled to Europe and Canada, while others settled in the United States, where anti-Iranian sentiment flared as the hostage crisis unfolded. For those who chose America, Texas became the fourth-largest settlement area. Iranians in Texas culls data, interviews, and participant observations in Iranian communities in Houston, Dallas, and Austin to reveal the difficult, private world of cultural pride, religious experience, marginality, culture clashes, and other aspects of the lives of these immigrants. Examining the political nature of immigration between Iran and the United States and social, cultural, and economic life for Iranian immigrants and their American-born children, Mohsen Mostafavi Mobasher incorporates his own experience as a Texas scholar born in Iran. In this revised edition, two new chapters and a new introduction and conclusion provide updates on what has happened in the Obama, Trump, and Biden administrations, including the Iran nuclear deal and resulting controversy, the Muslim ban, and the global protests over the death of twenty-two-year-old Mahsa Amini for not wearing a hijab. Bringing to life a unique immigrant population in the context of global politics, Iranians in Texas overturns stereotypes and echoes diverse voices.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".