Elliot Ackerman & Omar El Akkad: Live at Politics and Prose
Bibliographic record
Abstract
Elliot Ackerman & Omar El Akkad discuss their books, "Dark at the Crossing" and "American War" respectively, at Politics and Prose on 3/6/18. Ackerman served multiple tours of duty in Iraq and Afghanistan, experiences that lent urgency and power to his debut novel, Green on Blue . His second novel, a National Book Award finalist, now in paperback, draws on his years of reporting about the Syrian civil war. The story focuses on Haris Abadi, an Iraqi who became an American citizen after serving as an interpreter for American troops in his home country. Dissatisfied with his new life and looking for purpose, he decides to join the fight against the al-Assad regime. The border between Turkey and Syria is closed, and as Haris searches for a way to cross, he relives the brutal interrogations he participated in with the Americans and meets a Syrian refugee determined to return to Aleppo to find the daughter she’s sure has survived. https://www.politics-prose.com/book/9781101971550 El Akkad’s powerful debut novel, just released in paperback, opens in the U.S. of 2074, a nation once again at war with itself. Against the backdrop of a government in crisis, a crippling shortage of fuels, and a plague that has half the country in quarantine, El Akkad tells the life story of Sarat. Sent to a refugee camp at age six, she’s trained as a guerilla fighter by agents of the Middle Eastern Bouazizi Empire. She carries out assassinations, is captured, tortured, and survives to continue the struggle. El Akkad, a Cairo-born Canadian journalist, has taken the stuff of recent events—suicide bombers, drone strikes, Guantánamo, hate crimes—and turned it into a chilling dystopian fable. https://www.politics-prose.com/book/9781101973134
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.534 | 0.049 |
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; both teacher heads agree on what is shown here.
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".