Diaspora and Abjection of a Nowhere in Particular: Theorizing the Hyphen in Iranian-Canadian Narratives
Bibliographic record
Abstract
Iranian-Canadian literature has yet to be examined through the lens of diaspora studies and as a constituency of Canadian literature. Even though the number of Iranian immigrants and international students has increased markedly in Canada since the 1979 Revolution, we have barely heard about the Iranian-Canadian diaspora. In this project, I intend to look into selected Iranian-Canadian literature in order to investigate how and in what ways this hyphenated literature has helped the Iranian “imagined community” in Canada create a diasporic re-collectivity. The main reason for the absence of scholarship on Iranian-Canadian literature is that as a “neither-nor” society, to use Afsaneh Najmabadi’s words, Iran has barely experienced colonization, slavery, or indentured labour, as a result of which it cannot easily be incorporated into postcolonial studies. Any negligence toward a diasporic literature based on both home and host countries as constituents of the diaspora’s hyphenated identity eclipses the significance of interrelations between the two societies. By doing this research, I hope to decontextualize Iranian diasporic literature from its American context and decenter the importance of the veil and Orientalist discourse, which have been the main perspectives within which Iranian diasporic literature has been often defined and discussed. Chapter 1 discusses Fereshteh Molavi’s Thirty Shadow Birds (2019) to show how Molavi creates a hyphenated diasporic identity for Iranian-Canadian populations mainly through Persian literary conventions. Chapter 2 investigates the role of memoirs in Iranian-Canadian literature to differentiate Iranian-Canadian memoirs from the Iranian American by looking into Maziar Bahari’s Then They Came for Me: A Family’s Story of Love, Captivity, and Survival (2011); this chapter demonstrates how Bahari degenders the genre of memoir in the Iranian diasporic literature and depicts the process of (en)gendering the West as a form of resistance for Iranian politicians against imperialism. Chapter 3 discusses Ava Homa’s Daughters of Smoke and Fire (2020) to delineate how the national identity devised by the Islamic government of Iran has suppressed religious and linguistic minorities. Against this background and by challenging the Aryan race as the pillar of Iranian national identity, authors like Homa have tried to make through Iranian-Canadian literature a diasporic space inclusive of Iran’s minoritized ethnicities. My goal in this project is to start the overdue conversation on Iranian-Canadian literature as an important component of Iranian diasporic studies.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.035 | 0.054 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".