Arthur Ferdinand Yencken: A British diplomat in wartime Spain

Abstract During the Second World War, the maintenance of Spanish neutrality was of great strategic importance to the British Government and its Allies. The British Embassy in Madrid therefore had a role of crucial significance. This article describes the part played by Arthur Yencken, deputy to Ambassador Hoare, who served in Spain from 24 April 1939 until his death on 18 May 1944. In uncovering Yencken’s contributions to the war effort, the article offers an example of the critical part played by diplomacy in achieving British foreign policy objectives. Analysis of unpublished official documents provides fresh perspectives on trade and other negotiations from 1940 to 1944 including those related to wheat, petroleum and wolfram supplies. These documents demonstrate the quality of Yencken’s diplomatic skills during negotiations with Spanish Foreign Minister Jordana when Yencken was Chargé d’Affaires in his Ambassador’s absence. The article also describes the circumstances of his death and the remarkable responses to it

Design and the living environment

Volume: 86Start Page: 215End Page: 22

Songlines and the Gondwanan Inheritance: Australia

Griffith Sciences, Griffith School of EnvironmentNo Full Tex

Environment, education and society in the Asia-Pacific: three key questions

Not availabl

Environment, Education and Society in the Asia-Pacific: Local traditions and Global discourses

Griffith Sciences, Griffith School of EnvironmentNo Full Tex

Environmental Attitudes, Knowledge and behavious of young people in the asia pacific region

Griffith Sciences, Griffith School of EnvironmentNo Full Tex

The Research

Griffith Sciences, Griffith School of EnvironmentNo Full Tex

Automatic classification of sentences to support Evidence Based Medicine

AIM: Given a set of pre-defined medical categories used in Evidence Based Medicine, we aim to automatically annotate sentences in medical abstracts with these labels. METHOD: We constructed a corpus of 1,000 medical abstracts annotated by hand with specified medical categories (e.g. Intervention, Outcome). We explored the use of various features based on lexical, semantic, structural, and sequential information in the data, using Conditional Random Fields (CRF) for classification. RESULTS: For the classification tasks over all labels, our systems achieved micro-averaged f-scores of 80.9% and 66.9% over datasets of structured and unstructured abstracts respectively, using sequential features. In labeling only the key sentences, our systems produced f-scores of 89.3% and 74.0% over structured and unstructured abstracts respectively, using the same sequential features. The results over an external dataset were lower (f-scores of 63.1% for all labels, and 83.8% for key sentences). CONCLUSIONS: Of the features we used, the best for classifying any given sentence in an abstract were based on unigrams, section headings, and sequential information from preceding sentences. These features resulted in improved performance over a simple bag-of-words approach, and outperformed feature sets used in previous work