Regulatory Transgression? Drivers, Aims and Effects of Money Laundering and Terrorism Financing Regulation in Pakistan
Bibliographic record
Abstract
The harmonization of money laundering and terrorism financing regulation is a key feature of the contemporary global economy. Since 9/11 particularly, the remarkable growth of this field of regulation has been characterized by both scale and intensity. However, this drive towards regulatory convergence is puzzling: the efficacy of the regulation remains unproven while the content of the regulation poses significant challenges to both criminal justice systems and human rights frameworks. The corollary to these observations: who does the regulation benefit? \n\nWith the understanding that all regulation is an expression of some interest/s, this study analyses the trajectory of this global regulation and its products. My aim is to understand who gains what from regulation and how they influence this regulatory evolution. Focusing on Pakistan, my research will examine how anti money laundering (AML) and counter terrorism financing (CTF) regulation and its increasing demands for information affects established power hierarchies in states, between states and among states. At the international and transnational levels, Im interested in how a universal financial regulation discourse threatens basic rights and freedoms and how this exercise of power affects civil, political and economic rights in a country, its foreign policy as well as geopolitics. At the national level, Im curious about how such regulatory power with its distinctive objectives interacts or conflicts with or even amplifies the control of established power centres in a polity. The analysis of power relations in the case of Pakistan will be particularly instructive for several reasons. First, the size of its formal economy is rivalled (if not surpassed) by the informal or black economy and the money laundering industry is all the more powerful for processing illicit funds from crime; corruption; and trade- and taxation-related malpractices. Second, Pakistans military establishment has long supported militancy as a foreign policy tool, both materially and financially, and to date orients its foreign policy accordingly. Finally, the military establishment also relies on intrusive surveillance tools to control civil society. \n\nThe opacity of the discourse regarding international financial governance makes a closer scrutiny of its aims a critical imperative. By exploring the links between regulation, power, knowledge and surveillance, I hope to understand the aims of this power and offer a critique of financial regulation as a technique of power and the politics of making and administering AML/ CTF regulation, both across the globe and within states.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".