Bibliographic record
Abstract
Offers an in-depth exploration of the Iranian diaspora and the negotiation of belonging With an estimated 5 to 8 million people spread across the globe, the Iranian diaspora has become a visible and dynamic cultural presence—especially in North America and Europe. Faced with shallow or distorted portrayals, diasporic Iranians have responded to persistent misrepresentations and marginalization by turning to culture as a deliberate strategy of inclusion. Reshaping how their stories are told, community organizers, artists, and entrepreneurs are actively challenging public narratives, asserting new cultural imaginaries, and navigating the terms of inclusion and exclusion, putting new visions of Iranian identity into the public eye. Drawing on transnational ethnographic fieldwork and over 125 semi-structured interviews conducted over the course of 16 years, Culture Beyond Country offers the first comparative ethnography of these cultural strategies of inclusion, examining the distinct practices and experiences of Iranians in three key cities of the diaspora, Los Angeles, Stockholm, and Toronto. Attending to the institutional and ideological forces that come to bear on Iranian cultural organizers in these three diasporic locations, Amy Malek examines how immigrants and their descendants negotiate belonging in response to various and shifting state approaches to cultural citizenship. The volume examines how state multicultural policy influences who is empowered to represent Iranian culture, how local factors shape expressions of Iranian identity across the diaspora, and how these representations are contested within Iranian communities. Providing a compelling transnational study of immigrant multiculturalism, cultural citizenship, and inclusion across the global Iranian diaspora, Culture Beyond Country showcases not only how the process of representing Iranian culture in the diaspora generates competing views on what it means to be Iranian, but also how competing modes of belonging subvert and reinforce existing power relations across local, national, and transnational scales.
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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 | 0.001 | 0.002 |
| 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 | 0.005 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".