Repeating the Mistakes of the Law and Development Movement in Afghanistan
Bibliographic record
Abstract
Abstract The rapid collapse of the Afghan state did not come as a surprise to those who are well-versed in the chequered history of the Law and Development Movement. While the Movement’s one-size-fits-all modernization project has been largely rejected, such misguided efforts continue under the aims of “building the Rule of Law” or “improving governance.” The fallout from the U.S. withdrawal from Afghanistan is a stark reminder for states and multilateral organizations not to overlook the lessons of the Movement that may have been obscured by the different banners under which state- and market-building efforts have been pursued. From the Movement’s sincere yet naïve efforts of state-building between the 1950s and 70s, to its swing to build and support markets under the Washington Consensus paradigm in the 80s and 90s, and a later emphasis on good governance through state institutions from the 2000s onwards, it is clear that top-down state-building efforts have had limited success. The paper argues that the failure of the Afghanistan mission may have been avoided if the U.S. had turned to the lessons learned from the law-and-institutions-building enterprises of the past 70 years. Instead, the failure to heed these lessons led to the building of a state akin to a house of cards. By overlooking the importance of embedded cultural institutions, the legitimacy of the state as perceived by its people, and the dynamic interaction between formal and informal institutions, the state-building project in Afghanistan was bound to fail. Following the takeover by the Taliban, the small gains made in Afghanistan over the past two decades on the issues of hunger, poverty, health, and education have seen rapid deceleration and require urgent attention. The critiques outlined in this paper, informed by the experience of the Law and Development Movement, are meant to inform, not discourage, global engagement in advancing the human development agenda in Afghanistan and other fragile contexts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".