Half-Baked: The Science and Politics of Legal Pot

Weed, herb, grass, bud, ganja, Mary Jane, hash oil, sinsemilla, budder, and shatter. Marijuana - whether viewed as a medicine or intoxicant - is fast becoming a part of everyday life, with the CDC reporting 7,000 new users every day and the American market projected to grow to $20 billion by 2020. Based on early campaign rhetoric, by that same year the U.S. could have a pro-marijuana president. Despite its growing acceptance and popularity, marijuana remains illegal under federal law. Like heroin, LSD, and ecstasy, marijuana is a DEA Schedule I drug reflecting a Congressional determination that marijuana is both overly addictive and medically useless. So what is the truth about pot? The current massive pro-marijuana momentum and increased use, obscures the fact that we still know almost nothing about marijuana\u27s treatment and palliative potential. Marijuana\u27s main psychoactive chemical is THC; but it also contains over 500 other chemicals with unknown physiological and psychological effects that vary based on dosage and consumption method. Medical marijuana may be legal in 32 states and supported by 84% of Americans, but federal constraints shield marijuana from basic scientific inquiry. This means that lawmakers and voters are enthusiastically supporting greater access to a drug without demanding critical scientific data. For policymaking purposes, this data should include marijuana\u27s short and long-term brain effects, possible lung and cardiac implications, chemical interactions with alcohol and other drugs, addiction risks, pregnancy and breast-feeding concerns, and the effects of secondhand smoke. This Article treats marijuana as a significant contemporary science and law problem. It focuses on the fundamental question of regulating a substance that has not been adequately researched. The Article examines the extant scientific data, deficiencies, and inconsistencies and explains why legislators should not rely on copycat laws governing alcohol or prescription narcotics. It also explores how marijuana\u27s hybrid federal (illegality)/state (legality) raises compelling theoretical and practical Constitutional questions of preemption, the anti-commandeering rule, and congressional spending power. Marijuana legalization has, thus far, been treated as a niche academic concern. This approach is short-sighted and narrowminded. Marijuana regulation implicates the reach of national drug policy, the depth of state sovereignty, and the shared obligation to ensure the health and safety of our citizenry

The Future of Neuroimaged Lie Detection and the Law

News broke during the last week of September 2008 that the Department of Homeland Security has begun testing a new mind-reading device. According to the Department’s website, the Human Factors Directorate of Science and Technology has been working on Future Attribute Screening Technology (FAST). Although design specifics have not been made public, the MALINTENT prototype is a device that rapidly and remotely measures subjects’ body temperature, heart rate, and respiration. MALINTENT then compares these measurements with a matrix of physiological norms to generate conclusions about each subject’s future dangerousness. MALINTENT’s designers and proponents believe that this new technology will reliably distinguish perspiring perambulators and fearful flyers from true terrorists. These claims are difficult to assess because the results of the only publically disclosed tests of MALINTENT were subsequently classified

Finding Nino: Justice Scalia\u27s Confrontation Clause Legacy from its (Glorious) Beginning to (Bitter) End

Until very recently, Justice Scalia has steered the Court’s modern confrontation jurisprudence. However, as discussed below, his leadership is increasingly threatened by deep divisions on questions of historical accuracy, constitutional interpretation, and the practical realities of twenty-first century criminal prosecutions

A fuzzy associative classification approach for recommender systems

Despite the existence of dierent methods, including data mining techniques, available to be used in recommender systems, such systems still contain numerous limitations. They are in a constant need for personalization in order to make effective suggestions and to provide valuable information of items available. A way to reach such personalization is by means of an alternative data mining technique called classification based on association, which uses association rules in a prediction perspective. In this work we propose a hybrid methodology for recommender systems, which uses collaborative altering and content-based approaches in a joint method taking advantage from the strengths of both approaches. Moreover, we also employ fuzzy logic to enhance recommendations quality and eectiveness. In order to analyze the behavior of the techniques used in our methodology, we accomplished a case study using real data gathered from two recommender systems. Results revealed that such techniques can be applied eectively in recommender systems, minimizing the eects of typical drawbacks they present