Persinate Selves: Memories of Place and Origin Before Nationalism, by Mana Kia
Bibliographic record
Abstract
Books begin with their titles.For me, the first task is to translate the title into Persian.I ask myself, "What do 'we' call it in Persian?"For Persianate Selves, this is not an easy translation.The adjective "Persianate" delineates a more expansive meaning than the term "Persian," and no word in the Persian language can stand solidly as an equivalent for it.This comprehensive meaning of Persian is what Mana Kia investigates in this book.Let me explain with a personal example.That I speak of "we Persians" comes from the fact that I was born from Persian parents in Iran and grew up speaking Persian.Based on the understanding of modern nationalism about ethnicity, territory, and language, I call myself a Persian.But was it the case for people before the rise of modern nationalism?Kia's argument serves to show that it was not.Not everyone who lived in Iran was Persian, nor was Persian ethnicity based only on blood and lineage, nor were "native" Persian speakers the only people considered to be Persian.Mana Kia reconceptualizes the meaning of origin and place for being Persian by focusing on people who lived in Iran and Hindustan in the eighteenth century.The book's temporal focus spans between two critical events: the fall of Safavid in 1722 and the production of Macaulay's famous memorandum "Minute Upon Indian Education" in 1835.The former is critical because it defined the shared meaning of place and origin and brought about the construction of our modern idea of Iran, while the latter is critical since it abolished Persian as the language of power in the subcontinent and thus transformed those shared meanings (20).
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.012 |
| Insufficient payload (model declined to judge) | 0.002 | 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".