Creating a spontaneous conversational speech corpus

Speech recognition and language analysis of spontaneous speech arising in naturally spoken conversations are becoming the subject of much research. However, there is a shortage of spontaneous speech corpora that are freely available for academics. We therefore undertook the building of a natural conversation speech database, recording over 200 hours of conversations in English by over 600 local university students. With few exceptions, the students used their own cell phones from their own rooms or homes to speak to one another, and they were permitted to speak on any topic they chose. Although they knew that they were being recorded and that they would receive a small payment, their conversations in the corpus are probably very close to being natural and spontaneous. This paper describes a detailed case study of the problems we faced and the methods we used to make the recordings and control the collection of these social science data on a limited budget.

Teaching and cognitive outcomes in A-levels and Advanced GNVQ's: Case studies from Science and Business Studies Classrooms

The purpose of the paper is to demonstrate how a research diary methodology, designed to analyse A-level and GNVQ classrooms, can be a powerful tool for examining pedagogy and quality of learning at the level of case study. Two subject areas, science and business studies, are presented as cases. Twelve teachers and thirty-four students were studied over a four-week period in May 1997 and contrasts were drawn between lessons from three A-level physics teachers/three Advanced GNVQ science teachers and two A-level business/economics teachers/four Advanced GNVQ business teachers. Lessons were analysed within a cognitive framework which distinguishes between conceptual and procedural learning and emphasizes the importance of metacognition and epistemological beliefs. Two dimensions of lessons were identified: pedagogical activities (e.g. teacher-led explanation, teacher-led guidance on a task, question/answer sessions, group discussions, working with IT) and cognitive outcomes (e.g. structuring and memorizing facts, understanding concepts and arguments, critical thinking, problem-solving, learning core skills, identifying values). Immediately after each lesson, teachers and students (three per class) completed structured research diaries with respect to the above dimensions. Data from the diaries reveal general and unique features of the lessons. Time-ofyear effects were evident (examinations pending in May), particularly in A-level classrooms. Students in business studies classes reported a wider range of learning activities and greater variety in cognitive outcomes than did students in science classes. Science students self-rating of their ability to manage and direct their own learning was generally low. The phenomenological aspects of the classrooms were consistently linked to teachers' lesson plans and what their teaching objectives were for those particular students at that particular time of the year.

Extracting Policy Positions from Political Texts Using words as Data

We present a new way of extracting policy positions from political texts that treats texts not as discourses to be understood and interpreted but rather, as data in the form of words. We compare this approach to previous methods of text analysis and use it to replicate published estimates of the policy positions of political parties in Britain and Ireland, on both economic and social policy dimensions. We “export” the method to a non-English-language environment, analyzing the policy positions of German parties, including the PDS as it entered the former West German party system. Finally, we extend its application beyond the analysis of party manifestos, to the estimation of political positions from legislative speeches. Our “language-blind” word scoring technique successfully replicates published policy estimates without the substantial costs of time and labor that these require. Furthermore, unlike in any previous method for extracting policy positions from political texts, we provide uncertainty measures for our estimates, allowing analysts to make informed judgments of the extent to which differences between two estimated policy positions can be viewed as significant or merely as products of measurement error.

Improved data for solar flare X-ray spectral analysis

The inclusion of collisional rates for He-like Fe and Ca ions is discussed with reference to the analysis of solar flare Fe XXV and Ca XIX line emission, particularly from the Yohkoh Bragg Crystal Spectrometer (BCS). The new data are a slight improvement on calculations presently used in the BCS analysis software in that the discrepancy in the Fe XXV y and z line intensities (observed larger than predicted) is reduced. Values of electron temperature from satellite-to-resonance line ratios are slightly reduced (by up to 1 MK) for a given observed ratio. The new atomic data will be incorporated in the Yohkoh BCS databases. The data should also be of interest for the analysis of high-resolution, non-solar spectra expected from the Constellation-X and Astro-E space missions. A comparison is made of a tokamak S XV spectrum with a synthetic spectrum using atomic data in the existing software and the agreement is found to be good, so validating these data for particularly high-n satellite wavelengths close to the S XV resonance line. An error in a data file used for analyzing BCS Fe XXVI spectra is corrected, so permitting analysis of these spectra.

Error sensitive historical GIS: Identifying areal interpolation errors in time series data

Historical GIS has the potential to re-invigorate our use of statistics from historical censuses and related sources. In particular, areal interpolation can be used to create long-run time-series of spatially detailed data that will enable us to enhance significantly our understanding of geographical change over periods of a century or more. The difficulty with areal interpolation, however, is that the data that it generates are estimates which will inevitably contain some error. This paper describes a technique that allows the automated identification of possible errors at the level of the individual data values.