Status of Developing Afghan Governance and Lessons for Future Endeavors
Bibliographic record
Abstract
Building the capacity of and reforming Afghan governance is widely viewed as the key to success in Afghanistan. Assessing progress, however, is hampered by limited data outside the Afghan security ministries – the Ministries of Defense and Interior – and by the lack of a common definition of governance. Available reporting suggests building governance capacity is far from complete. Varying definitions of governance, coupled with the use of the term by numerous organizations without defining it, results in addressing too broad a range of issues. It would be more useful to concentrate on the core of governance – providing the services the Afghan government has committed to provide to its citizens. This, in turn, requires that Afghan ministries have the functional capacity to carry out their responsibilities, including financial management, budget formulation and execution, policy and strategic planning, and service delivery. However, time is growing short. The Afghan experience provides some important lessons that could guide future endeavors for the international community. First, this paper discusses progress in building ministerial capacity. Second, it discusses recent efforts to link continued financial assistance to Afghanistan with improved governance. Third, it describes how the lack of a commonly accepted definition of governance complicates assessing progress. Finally, it offers conclusions and observations about the failure to establish an autonomous Afghan governance capacity. For more than a decade, improving governance has been recognized as the most difficult and critical challenge involving Afghan reconstruction. The Special Inspector General for Afghanistan Reconstruction (SIGAR) reports that U.S. policymakers have consistently identified building the capacity of and reforming Afghan governance as the key to success in Afghanistan (SIGAR 2012, 22).
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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 | 0.013 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".