Bibliographic record
Abstract
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, ultimately proving to be a place of paradox for any Middle Easterner in exile. 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 and how the originating and receiving countries shape the prospects of integration, Mohsen Mobasher incorporates his own experience as a Texas scholar born in Iran. Tracing current anti-Muslim sentiment to the Iranian hostage crisis, two decades before 9/11, he observes a radically negative shift in American public opinion that forced thousands of Iranians in the United States to suddenly be subjected to stigmatization and viewed as enemies. The book also sheds light on the transformation of the Iranian family in exile and some of the major challenges that second-generation Iranians face in their interactions with their parents. Bringing to life a unique population in the context of global politics, Iranians in Texas overturns stereotypes while echoing diverse voices.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".