NSU Distinguished Speakers Series Present: Maziar Bahari - October 10, 2012
Bibliographic record
Abstract
Bahari, an Iranian Canadian, served as a Newsweek correspondent for more than a decade, covering Iranian economic, political, and cultural life. In 2009, he was arrested in Tehran while reporting on the presidential election protests. Bahari was picked up by the Islamic Revolutionary Guard and sent to Evin Prison, which is notorious for torturing political prisoners. Accused of “masterminding the coverage of the Iranian election by the Western media” and being a spy for the Mossad, the MI6, and the CIA, Bahari was held in solitary confinement for most of his 118 days in captivity. He was repeatedly beaten and was forced to give a televised false confession. After international pressure mounted and U.S. Secretary of State Hillary Clinton expressed interest in his case, Bahari was released on $300,000 bail. Bahari’s story of resilience was the subject of a 60 Minutes segment and a Newsweek cover story. In 2011, he published a memoir detailing the overseas ordeal, titled, Then They Came for Me: A Family’s Story of Love, Captivity, and Survival. The book became a New York Times bestseller. Don Rosenblum Stephen Andon Maziar Bahari
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.278 | 0.098 |
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".