Data Collection in Pragmatics Research

Authentic discourse, elicited conversation, and roleplay are types of spoken interaction; production questionnaires, multiple-choice, and scaled response instuments are survey methods and thus obtain written responses when self-administered; interviews are a specific type of spoken interaction that may or may not be structured by a questionnaire; in its less structured forms, interviews produce narrative self-reports and axethusakintodiariesasastory-tellinggenre.Think aloud protocols can be related to interviews and diaries in that they, too, produce narrative self-reports; however, in their classic fornu they are on-line verbalizations of thought processes rather than stories. Verbal protocol shave their home in experimental psychology and are thus furthest removed from the conversational interaction in authentic talk activities that opened the list of data collection procedures in pragmatics. I shall now consider each procedure in tum

Variation in Interlanguage Speech Act Realization

COMPARED TO OTHER AREAS of second language research, interlanguage (IL) pragmatics is still a young discipline. The first studies into nonnative speakers' (NNS) perception and performance of speech acts appeared ten years ago, both in North America (e.g. Borkin and Reinhart 1978) and Europe (Hackmann 1977). Since then, a number of investigations into IL speech act realization have been conducted, examining how different types of speech acts are performed by NNSs with a variety of language backgrounds and target languages (cf. the overview in Blum-Kulka, House and Kasper, in press a). While the information collected by these empirical studies contributes significantly to our understanding of speech act realization across cultures and languages, it seems timely to take a more theoretical view of IL pragmatics, in order toreexamine some central notions and to suggest some directions for future research. This paper, then, has the following goals: (1) To provide some conceptual clarification of the notions 'pragmatics' and 'speech act', and to determine the type of variability that is most interesting in the context of IL pragmatics. (2) To identify NNSs' learning tasks in their acquisition of pragmatic knowledge, as a prerequisite for outlining some of the central research tasks for IL pragmaticists. (3) Based on some results from a descriptive study into IL speech act realization, to discuss what further research questions such results suggest with regard to explaining variability in IL pragmatics

Pragmatics & Language Learning, Volume 12

Pragmatics & Language Learning Volume 12 examines the organization of second language and multilingual speakers’ talk and pragmatic knowledge across a range of naturalistic and experimental activities. Based on data collected on Danish, English, Hawaiʻi Creole, Indonesian, and Japanese as target languages, the contributions explore the nexus of pragmatic knowledge, interaction, and L2 learning outside and inside of educational settings. Pragmatics & Language Learning (“PLL”), a refereed series sponsored by the National Foreign Language Resource Center at the University of Hawaiʻi, publishes selected papers from the biennial Conference on International Pragmatics & Language Learning under the editorship of the conference hosts and the series editor, Gabriele Kasper

Comparing Apples with Apples: Robust Detection Limits for Exoplanet High-Contrast Imaging in the Presence of non-Gaussian Noise

Over the past decade, hundreds of nights have been spent on the worlds largest telescopes to search for and directly detect new exoplanets using high-contrast imaging (HCI). Thereby, two scientific goals are of central interest: First, to study the characteristics of the underlying planet population and distinguish between different planet formation and evolution theories. Second, to find and characterize planets in our immediate Solar neighborhood. Both goals heavily rely on the metric used to quantify planet detections and non-detections. Current standards often rely on several explicit or implicit assumptions about the noise. For example, it is often assumed that the residual noise after data post-processing is Gaussian. While being an inseparable part of the metric, these assumptions are rarely verified. This is problematic as any violation of these assumptions can lead to systematic biases. This makes it hard, if not impossible, to compare results across datasets or instruments with different noise characteristics. We revisit the fundamental question of how to quantify detection limits in HCI. We focus our analysis on the error budget resulting from violated assumptions. To this end, we propose a new metric based on bootstrapping that generalizes current standards to non-Gaussian noise. We apply our method to archival HCI data from the NACO-VLT instrument and derive detection limits for different types of noise. Our analysis shows that current standards tend to give detection limit that are about one magnitude too optimistic in the speckle-dominated regime. That is, HCI surveys may have excluded planets that can still exist.Comment: After first iteration with the referee, resubmitted to AJ. Comments welcome

Quinoa Phenotyping Methodologies: An International Consensus

Quinoa is a crop originating in the Andes but grown more widely and with the genetic potential for significant further expansion. Due to the phenotypic plasticity of quinoa, varieties need to be assessed across years and multiple locations. To improve comparability among field trials across the globe and to facilitate collaborations, components of the trials need to be kept consistent, including the type and methods of data collected. Here, an internationally open-access framework for phenotyping a wide range of quinoa features is proposed to facilitate the systematic agronomic, physiological and genetic characterization of quinoa for crop adaptation and improvement. Mature plant phenotyping is a central aspect of this paper, including detailed descriptions and the provision of phenotyping cards to facilitate consistency in data collection. High-throughput methods for multi-temporal phenotyping based on remote sensing technologies are described. Tools for higher-throughput post-harvest phenotyping of seeds are presented. A guideline for approaching quinoa field trials including the collection of environmental data and designing layouts with statistical robustness is suggested. To move towards developing resources for quinoa in line with major cereal crops, a database was created. The Quinoa Germinate Platform will serve as a central repository of data for quinoa researchers globally.EEA FamailláFil: Stanschewski, Clara S. King Abdullah University of Science and Technology. Center for Desert Agriculture, Biological and Environmental Sciences and Engineering Division; Arabia SauditaFil: Rey, Elodie. King Abdullah University of Science and Technology. Center for Desert Agriculture, Biological and Environmental Sciences and Engineering Division; Arabia SauditaFil: Fiene, Gabriele. King Abdullah University of Science and Technology. Center for Desert Agriculture, Biological and Environmental Sciences and Engineering Division; Arabia SauditaFil: Craine, Evan B. Washington State University. Department of Crop and Soil Sciences; Estados UnidosFil: Wellman, Gordon. King Abdullah University of Science and Technology. Center for Desert Agriculture, Biological and Environmental Sciences and Engineering Division; Arabia SauditaFil: Melino, Vanessa J. King Abdullah University of Science and Technology. Center for Desert Agriculture, Biological and Environmental Sciences and Engineering Division; Arabia SauditaFil: Patiranage, Dilan S.R. King Abdullah University of Science and Technology. Center for Desert Agriculture, Biological and Environmental Sciences and Engineering Division; Arabia SauditaFil: Patiranage, Dilan S.R. Christian-Albrechts-University of Kiel. Plant Breeding Institute; AlemaniaFil: Johansen, Kasper. King Abdullah University of Science and Technology. Water Desalination and Reuse Center; Arabia SauditaFil: Schmöckel, Sandra M. University of Hohenheim. Institute of Crop Science. Department Physiology of Yield Stability; AlemaniaFil: Erazzu, Luis Ernesto. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Famaillá; Argentina.Fil: Tester, Mark. King Abdullah University of Science and Technology. Center for Desert Agriculture, Biological and Environmental Sciences and Engineering Division; Arabia Saudit