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Record W206675796

Detect, Disrupt, and Detain: Local Law Enforcement's Critical Roles in Combating Homegrown Extremism and the Evolving Terrorist Threat

2016· article· en· W206675796 on OpenAlexaboutno aff
Mitch Silber, A. J. Frey

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicLaw, Rights, and Freedoms
Canadian institutionsnot available
Fundersnot available
KeywordsTerrorismRadicalizationLaw enforcementLawStatutePolitical scienceAl qaedaHomeland securityCriminologySociology
DOInot available

Abstract

fetched live from OpenAlex

Introduction I. The Al-Qaeda Threat to the Homeland 2013 A. Al-Qaeda Core B. Affiliates and Allies C. Al-Qaeda Inspired (or Homegrown) II. Radicalization and Detection A. The Radicalization Process B. Online Radicalization III. Local Law Enforcement's Role A. Local Law Enforcement's Comparative Advantages 1. Manpower 2. General Police Power/Knowledge of the Community 3. Greater Accountability to Local Concerns B. The Legal Framework: The NYPD and Handschu: A Case Study 1. Background 2. Investigations Under Current Handschu Guidelines a. Leads b. Preliminary Inquiries c. Full Investigations d. Terrorism Enterprise Investigations e. Investigative Techniques 3. Other Authorizations Under Handschu IV. Prosecution A. State Level Prosecutions 1. Ahmed Ferhani 2. Jose Pimentel B. Federal Criminal Statutes 1. Background 2. Dissemination of Bomb Making Material/Information 3. Solicitation 4. Communicating Threats Conclusion As this thing metastasizes, cops are it. We are going to win this at the local level. (1) INTRODUCTION Over the last dozen years, the great cities of the West--New York, London, Madrid, Amsterdam, Boston, Toronto, Sydney, and Los Angeles, among others--have been under the almost constant threat of al-Qaeda type (2) terrorism. (3) There have been many plots against American cities. (4) Some have been planned and directed from al Qaeda or its affiliates abroad, whereas others have been hatched by small cells of so-called terrorists and/or lone wolves inspired by al-Qaeda's ideology. (5) And, while the vast majority of these plots have been thwarted, some have succeeded with deadly impact. (6) As the recent al-Qaeda-inspired terrorist attack in Boston of April 2013 demonstrated, despite the death of Osama bin Laden, the al-Qaeda type threat to the U.S. homeland--and cities in particular--remains both real and deadly. (7) Given that terrorist threats to urban environments are unlikely to abate any time soon, and that cities must seek to protect their citizens from terrorism, local police departments have to consider how best to counter this menace. At the same time, local police departments must balance the competing challenges that urban counterterrorism initiatives raise from security, law enforcement, intelligence and civil liberties perspectives. More broadly, local law enforcement has to understand the nature of the threat, which necessarily informs how it should be best thwarted. This Article argues that the threat is three-fold: from al-Qaeda Core; al-Qaeda's regional affiliates and allies; and extremists. Moreover, as U.S. military and intelligence operations overseas continue to put pressure on the first two elements, the threat is likely to metastasize and become further decentralized? While the threat from al-Qaeda Core and its overseas affiliates and allies will remain, we have seen over the last five to seven years that these so-called homegrown extremists--who are radicalized here in the United States, often in urban centers and often over the Internet--present one of the most serious terrorism threats to the homeland. (9) This Article will focus on the third leg of the stool: the threat of extremists. In particular, it addresses some of the problems this phenomenon presents, as well as the tools available to law enforcement and intelligence agencies to combat it in urban environments. Finally, it will focus in particular on the role of local law enforcement in combating this threat. Part I of this Article begins by describing and defining the nature of the al-Qaeda threat in general, and that of extremism in particular. Part II then addresses the question of radicalization--the process by which extremists may be moved to violence. …

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.005
Scholarly communication0.0080.003
Open science0.0010.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0130.002

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.

Opus teacher head0.012
GPT teacher head0.273
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2016
Admission routes1
Has abstractyes

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