Weblogistan Goes to War: Representational Practices, Gendered Soldiers and Neoliberal Entrepreneurship in Diaspora
Bibliographic record
Abstract
In this article, which is based on twenty four months of combined online and off-line ethnographic research, I show the way that some Iranian diasporic bloggers use their weblogs as entrepreneurship resources during the ‘war on terror’. Through a discourse analysis of a documentary film about Weblogistan and interviews with diasporic Iranian bloggers in Toronto, I argue that Weblogistan is implicated in discourses of militarism and neoliberalism that interpellate the representable Iranian blogger as a gendered neoliberal homo oeconomicus. The production of knowledge about Iran in transnational encounters between the media, think tanks, policy institutions and the Iranian diasporic self-entrepreneurs, relies on gendered civilizational discourses that are inherently tied to the ‘war on terror’. Following feminist scholars who have theorized militarism and gender, I argue that dominant representations of Weblogistan produce different gendered subject positions for Iranian bloggers. Although the masculine blogger soldier takes freedom to Iran through his active participation in proper politics (enabled by his freedom of speech in North America and Europe), the woman blogger finds freedom of expression in writing about sex and telling the truth of her sex in a confessional mode. It is in this war of representation that women bloggers negotiate their subjectivity while shuttling in and out of local and global politics, as subjects of politics (markers of freedom and oppression) and political abjects (not worthy of political participation).
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".