Poppies for Peace: Reforming Afghanistan's Opium Industry
Afghanistan, which could be slipping back into chaos, must turn the tables on the opium crisis. The international community should establish a pilot project and investigate a licensing scheme to legalize the production of medicines such as morphine and codeine from poppy crops to help it escape.
Poppies for Medicine in Afghanistan: Historical Lessons from India and Turkey
This study examines India and Turkey as case studies relevant to the Senlis Council’s ‘poppies for medicine’ proposal. The proposal is that Afghan farmers are licensed to produce opium for medical and scientific purposes. Here it is posited that the Senlis proposal neglects at least three key lessons from the Turkish and Indian experiences. First, not enough weight has been given to diversion from licit markets, as experienced in India. Second, both India and Turkey had significantly more efficient state institutions with authority over the licensed growing areas. Third, the proposal appears to overlook the fact that Turkey’s successful transition was largely due to the use of the poppy straw method of opium production. It is concluded that, while innovative and creative policy proposals such as that of the Senlis proposal are required if Afghanistan is to move beyond its present problems, ‘poppies for medicine’ does not withstand evidence-based scrutiny.
Mining Words in the Minds of Second Language Learners: Learner-Specific Word Difficulty
While there have been many studies on measuring the size of learners’ vocabulary or the vocabulary they should learn, there have been few studies on what kind of words learners actually know. Therefore, we investigated theoretically and practically important models for predicting second language learners’ vocabulary and propose another model for this vocabulary prediction task. With the current models, the same word difficulty measure is shared by all learners. This is unrealistic because some learners have special interests. A learner interested in music may know special music-related terms regardless of their difficulty. To solve this problem, our model can define a learner-specific word difficulty measure. Our model is also an extension of these current models in the sense that these models are special cases of our model. In a qualitative evaluation, we defined a measure for how learner-specific a word is. Interestingly, the word with the highest learner-specificity was “twitter”. Although “twitter” is a difficult English word, some low-ability learners presumably knew this word through the famous micro-blogging service. Our qualitative evaluation successfully extracted such interesting and suggestive examples. Our model achieved an accuracy competitive with the current models.