Bibliographic record
Abstract
As a journalist for The Globe and Mail for 10 years, El Akkad has reported on war and conflict from around the world, including the war in Afghanistan, the military trials at Guantanamo Bay, the Arab Spring revolution in Egypt, and the Black Lives Matter movement in Ferguson, Missouri. In 2018, El Akkad's debut novel, American War, was shortlisted for a number of prominent literary prizes, garnering public attention as a finalist on the CBC Canada Reads competition. In this conversation, we talk to El Akkad about his novel, journalism, literary influences, migrations, and political visions of the future. One recurring theme in the conversation is the relationship between violence and the production of uncertainty—the unpredictability movement and refuge for the displaced; the ambiguity and risks of racial representation; the secrecy of detention and redaction; and the uncertainties of the future in times of change and crisis.
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 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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.045 |
| Scholarly communication | 0.021 | 0.024 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".