Bibliographic record
Abstract
The last six weeks has brought some of the worst violence in Afghanistan since 2001. In 2007, there were more than 230 Improvised Explosive Device (IED) attacks and 145 suicide attacks. Casualty rates were at least 25 percent higher in 2007 than the previous year. In the past 18 months, IED attacks have targeted numerous police and army busses, a group of legislators outside a factory at Baghlan, a five-star hotel in Kabul, and a Canadian convoy near a busy marketplace. The trends show that attacks are increasing in number and becoming more violent and dreadful to the Afghan population. The Taliban traditionally limited attacks to military and security-related officials, such as ISAF, ANA, ANP, and local militia forces. Recently, however, the Taliban have publicly stated their intent to broaden their range of targets to include those with heavy foreign exposure -- hotels, restaurants, businesses, and areas considered "soft" targets. At the same time, civilian casualties are becoming more palatable. This is in direct contrast to prior Taliban methods of insurgency which did not usually target civilians. To counter their psychological operations, ISAF could extend coordinated information operations to rally the local population behind the Afghan government and against the Taliban. Building an information campaign directed at the local population who is primarily anti-Taliban and nonviolent, is an essential part of minimizing future Taliban and Al Qaeda attacks.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.010 |
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".