Bibliographic record
Abstract
I was born in the district of Khakrez, in relatively secure times a two-hour drive north-west of Kandahar City. It was 1980, the year after the Soviets began unevenly occupying Afghanistan. When I was still young, my family and I, like millions of others, fled to Pakistan and began the difficult life of refugees. Most of the time we lived in Quetta, the capital of Baluchistan, the huge, underpopulated province across the border from Kandahar. In 2001 I returned to Kandahar with Akrem Khakrezwal, and had the privilege of working as his assistant as he became chief of police, successively, in Kandahar, Mazar-i Sharif and Kabul. On a visit home to Kandahar in 2005, he was killed by a huge bomb planted in a mosque. This crime was never seriously investigated. I then went to work as a governance adviser for the Canadian provincial reconstruction team. Two years later my boss there, a kind man approaching retirement named Glynn Berry, was killed by another bomb. It happened in district 5 of Kandahar City. A man was detained for that murder but Governor Khalid released him on the recommendation of Ahmed Wali Karzai, President Karzai's half-brother and head of Kandahar's provincial council. I, like everyone from Kandahar, have seen a lot of injustice. And yet, despite injustice being usual, it has never become a norm; we have continued to expect and yearn for better.
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 | 0.001 | 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 | 0.016 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.064 | 0.014 |
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".