Mathematical Analysis on the Data of Security Situation in Pakistan 2009-2013
Bibliographic record
Abstract
In 2009, Pakistan army launched two armed operations to clean up the Tehrik-e-Taliban Pakistan in Federally Administered Tribal Areas and North West Frontier Province (In 2010, Pakistan parliament approved a resolution: the North West Frontier Province officially changed its name to the Khyber-Pakhtunkhwa Province.). At the same time, the security situation in Pakistan began to worsen rapidly. Although the Pakistan army gradually achieved results by military offensive, but did not significantly improve the security situation in Pakistan. Because its strength was much weaker than Pakistan army, the Tehrik-e-Taliban Pakistan didn’t wish to make head-on confrontation with Pakistan army. Thus the Tehrik-e-Taliban Pakistan and Pakistan army both observably reduced their losses. But the Tehrik-e-Taliban Pakistan transferred its targets of retaliation from Pakistan army to Pakistan civilian, resulting in a substantial increase in Pakistan civilian casualties. So, Pakistan counter-terrorism objectives should focus on the protection of civilians, preventing Pakistan from the Tehrik-e-Taliban Pakistan hitting civilian targets. If Pakistan could maintain the social stability, it will have more possibilities to win final victory in the war on terror.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".