Bibliographic record
Abstract
Years ago, a young man was interviewed by the Canadian Security Intelligence Service (CSIS) at my office. He was flagged for his “anti-Canadian” views for opposing Ottawa’s involvement in Afghanistan. He had left Canada as an ardent supporter of Western intervention, but returned a security “threat” for his opposition.\nExtended family and friends killed or injured as “collateral damage” was the game changer. Intended or unintended, the dead are no less dead because we meant well, he observed. His story of radicalization is not unique.\n“With respect, you cannot continue to behave as if innocent deaths like those in my family are irrelevant,” wrote Faisal bin Ali Jabar in a letter addressed to then president Barack Obama in 2014. Jabar, who lost two relatives in a 2012 drone strike in Yemen, hit the target when he concluded, “you will defeat your own counterterrorism aims.”
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 | 0.007 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.013 | 0.011 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".