R. Pilgram

Strategisch manoeuvreren in medische consultatie: Een pragma-dialectische analyse van autoriteitsargumentatie van de arts

In dit artikel wordt autoriteitsargumentatie van de arts in medische consultatie geanalyseerd als strategische manoeuvre. Het doel van deze analyse is ten eerste om te laten zien hoe de institutionele context van het medische consult een argumentatieve discussie tussen een arts en een patient kan beinvloeden, en ten tweede om een uitgangspunt te bieden voor de evaluatie van de deugdelijkheid van autoriteitsargumentatie van de arts in medische consultatie. Om deze doelen te verwezenlijken, zal eerst een argumentatieve karakterisering van medische consultatie worden gegeven. Vervolgens zal autoriteitsargumentatie van de arts worden geanalyseerd aan de hand van de uitgebreide pragma-dialectische argumentatietheorie.

Reliability in content analysis: The case of semantic feature norms classification

Semantic feature norms (e.g., STIMULUS: car → RESPONSE: ) are commonly used in cognitive psychology to look into salient aspects of given concepts. Semantic features are typically collected in experimental settings and then manually annotated by the researchers into feature types (e.g., perceptual features, taxonomic features, etc.) by means of content analyses—that is, by using taxonomies of feature types and having independent coders perform the annotation task. However, the ways in which such content analyses are typically performed and reported are not consistent across the literature. This constitutes a serious methodological problem that might undermine the theoretical claims based on such annotations. In this study, we first offer a review of some of the released datasets of annotated semantic feature norms and the related taxonomies used for content analysis. We then provide theoretical and methodological insights in relation to the content analysis methodology. Finally, we apply content analysis to a new dataset of semantic features and show how the method should be applied in order to deliver reliable annotations and replicable coding schemes. We tackle the following issues: (1) taxonomy structure, (2) the description of categories, (3) coder training, and (4) sustainability of the coding scheme—that is, comparison of the annotations provided by trained versus novice coders. The outcomes of the project are threefold: We provide methodological guidelines for semantic feature classification; we provide a revised and adapted taxonomy that can (arguably) be applied to both concrete and abstract concepts; and we provide a dataset of annotated semantic feature norms.