Der Beitrag beschreibt einen Ansatz systematischer, regelgeleiteter qualitativer Analyse von Text, der methodische Starken der quantitativen Inhaltsanalyse teilweise ubernimmt und zu einem qualitativ orientierten Instrumentarium ausweitet. Dazu werden historische Entwicklungslinien der Inhaltsanalyse aufgezeigt und die Grundlagen der Technik (Analyseeinheiten, Schrittmodelle, Arbeiten mit Kategoriensystemen, Gutekriterien) expliziert. Schlieslich werden an Techniken Qualitativer Inhaltsanalyse die induktive Kategorienentwicklung und die deduktive Kategorienanwendung naher dargestellt. Es wird gezeigt, wo Computerprogramme diese qualitativen Analyseschritte unterstutzen konnen, es werden Ansatzpunkte quantitativer Auswertungsschritte festgemacht und abschliesend die Moglichkeiten und Grenzen des Ansatzes diskutiert.
URN: urn:nbn:de:0114-fqs0002204

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Qualitative Content Analysis

GROUNDING It takes two people working together to play a duet, shake hands, play chess, waltz, teach, or make love. To succeed, the two of them have to coordinate both the content and process of what they are doing. Alan and Barbara, on the piano, must come to play the same Mozart duet. This is coordination of content. They must also synchronize their entrances and exits, coordinate how loudly to play forte and pianissimo, and otherwise adjust to each other's tempo and dynamics. This is coordination of process. They cannot even begin to coordinate on content without assuming a vast amount of shared information or common ground-that is, mutual knowledge, mutual beliefs, and mutual assumptions And to coordinate on process, they need to update their common ground moment by moment. All collective actions are built on common ground and its accumulation. We thank many colleagues for discussion of the issues we take up here.

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Grounding in communication

A set of hypotheses is formulated for a connectionist approach to cognitive modeling. These hypotheses are shown to be incompatible with the hypotheses underlying traditional cognitive models. The connectionist models considered are massively parallel numerical computational systems that are a kind of continuous dynamical system. The numerical variables in the system correspond semantically to fine-grained features below the level of the concepts consciously used to describe the task domain. The level of analysis is intermediate between those of symbolic cognitive models and neural models. The explanations of behavior provided are like those traditional in the physical sciences, unlike the explanations provided by symbolic models.

On the proper treatment of connectionism

Extant literature has increased our understanding of specific aspects of digital transformation, however we lack a comprehensive portrait of its nature and implications. Through a review of 282 works, we inductively build a framework of digital transformation articulated across eight building blocks. Our framework foregrounds digital transformation as a process where digital technologies create disruptions triggering strategic responses from organizations that seek to alter their value creation paths while managing the structural changes and organizational barriers that affect the positive and negative outcomes of this process. Building on this framework, we elaborate a research agenda that proposes [1] examining the role of dynamic capabilities, and [2] accounting for ethical issues as important avenues for future strategic IS research on digital transformation.

Understanding digital transformation: A review and a research agenda

/ have given the term pragmatic failure' to the inability to understand 'what is meant by what is said'. In this paper I argue that pragmatic failure is an area of cross-cultural communication breakdown which has received very little attention from language teachers. I suggest that there is one area of pragmatic failure ('pragmalinguistic failure') which is fairly easy to overcome. It is simply a question of highly conventionalized usage which can be taught quite straightforwardly as 'part of the grammar'. The second area ('sociopragmatic failure') is much more difficult to deal with, since it involves the student's system of beliefs as much as his/her knowledge of the language. I argue that it is essential to avoid prescriptivism in this very sensitive area of language in use. To do so we must draw on insights from theoretical pragmatics and develop ways of heightening and refining students' metapragmatic awareness, so that they are able to express themselves as they choose.

https://rodas5.us.es/file/a26cd06f-d80f-47b8-a5f0-fb23430aa5fc/1/thomas_failure_wimba_SCORM.zip/files/thomas_pragmatic_failure.pdf

Cross-Cultural Pragmatic Failure

List of Figures. Preface. Acknowledgements. Introduction: What is Cognitive Science?. Broad Construal. Narrow Construal. Cognition: Broad and Narrow. Computation: Broad and Narrow. The Working Conception of Cognitive Science. Appendix: 1978 Sloan Report. Study Questions. Suggested Reading. Part I: Historical Background:. Introduction. 1. Associationism. Introduction: What is Associationism?. Generic Empiricist Associationism. Varieties of Associationism. Locke and James. The End of Classical Associationism. Study Questions. Suggested Reading. 2. Behaviorism and Cognitivism. Introduction. The Rise of Behaviorism and Stimulus-Response Psychology. Challenges to Behaviorism and Stimulus-Response Psychology. Cognitivism: Information Processing Psychology. Study Questions. Suggested Reading. 3. Biological Background. Introduction. Brain Ventricles vs. Brain Substance. Cortical Localization vs. Holism. Nerve Net Theory vs. the Neuron Doctrine. The First Half of the Twentieth Century. Study Questions. Suggested Reading. 4. Neuro-Logical Background. Introduction. Neural Networks and the Logic of Propositions. Perceptrons. Linear Separability and XOR: McCulloch and Pitts Nets and Perceptrons. Simple Detector Semantics. Study Questions. Suggested Reading. Part II: The Digital Computational Theory of Mind:. Introduction. 5. A Sample Artificial Intelligence Model: SHRDLU. Introduction. SHRDLU Dialogue. The Program. Limitations. Historical Role of SHRDLU. Study Questions. Suggested Reading. 6. Architecture(s). Introduction: Some Preliminary Concepts. Turing Machines. von Neumann Machines. Production Systems. Intermezzo: Pandemonium. Taxonomizing Architectures (I). Study Questions. Suggested Reading. 7. Representation(s). Introduction: The Variety of Representations: Some Standard High Level Formats. The Nature of Digital Computational Representation. Interpretational Semantics. Study Questions. Suggested Reading. 8. The Digital Computational Theory of Mind. Introduction. From the Representational Theory of Mind to the Computational Theory of Mind. The Digital Computational Theory of Mind and the Language of Thought. DCTM and the Mind-Body Problem. DCTM and Representational Content. DCTM and Consciousness (I). Modular (Cognitive) Architectures. Appendix: Modularity: Gall vs. Fodor. Study Questions. Suggested Reading. 9. Criticisms of the Digital Computational Theory of Mind. Introduction: The Turing Test (Again). Against Strong AI: Searle and the Chinese Room. The Digital Computational Mind in the Chinese Room. The DCTM and Consciousness (II). The DCTM and Mental Content. Against Cognitivism. DCTM Hardward and the Brain. The Domain and Scope of the DCTM. Study Questions. Suggested Reading. Part III: Connectionist Computational Theory of Mind:. Introduction. 10. Sample Connectionist Networks. Introduction. Jets and Sharks. NETtalk. Study Questions. Further Reading. 11. Connectionism: Basic Notions and Variations. Introduction. Basic Notions and Terminology. Learning and Training. Representation(s). Generic Connectionism. Study Questions. Suggested Reading. 12. The Connectionist Computational Theory of Mind. Introduction. The Connectionist Computational Theory of Mind. Motivations for the CCTM. A Bit of History: Connectionism and Associationism. Interpreting Connectionism: PTC. Taxonomizing Architectures (II). Appendix: Connectionism and Turing's Unorganized Machines. Study Questions. Suggested Reading. 13. Criticisms of the Connectionist Computational Theory of Mind. Introduction. Differences: The CCTM and the Brain. CCTM: Lures of Connectionism. CCTM and The Chinese Gym. CCTM and Propositional Attitudes. CCTM Detector Semantics. CCTM: Problems and Prospects. Study Questions. Suggested Reading. Coda: Computation for Cognitive Science or What IS a Computer, Anyway?. Introduction. Functional View of Computers. Levels of Description View of Computers. Combined Functional-Descriptive View of Computers. Levels of Computation: Stabler. Digital and Connectionist Computers. Is Everything a Computer?. Study Questions. Suggested Reading. Bibliography. Index.

Minds, Brains, Computers: An Historical Introduction to the Foundations of Cognitive Science

Meaning and speech acts: A review of: Jerold J. Katz, Propositional structure and illocutionary force☆

Part 1 Meaning, speech acts, and communication: introduction, Kent Bach meaning, H.P. Grice locutionary, illocutionary, perlocutionary, J.L. Austin intention and convention in speech acts, P.F. Strawson logic and conversation, H.P. Grice how performatives work, J.R. Searle. Part 2 Sense and reference: introduction, Nathan Salmon of names, J.S. Mill on sense and reference, G. Frege on denoting, B. Russell reference and definite descriptions, K. Donnellan naming and necessity, S. Kripke the meaning of "meaning", H. Putnam demonstratives, D. Kaplan. Part 3 Opacity and attributions of attitude: introduction, Graeme Forbes referential opacity, W. Quine a puzzle about belief, S. Kripke individualism and the mental, T. Burge frege's puzzle, N. Salmon. Part 4 Meaning and Truth: introduction, Scott Soames the thought - a logical enquiry, G. Frege the semantic conception of truth and the foundations of semantics, A. Tarski what is Tarski's theory of truth?, H. Field truth and meaning, D. Davidson afterward - truth and paradox, Van McGee.

Basic Topics in the Philosophy of Language

Linguistics : an introduction to language

Grice proposed to investigate ‘the total signification of the utterance’. One persistent criticism of Grice’s taxonomy of signification is that he missed an important category of information. This content, and/or the process of providing it, goes by a variety of labels: ‘generalized implicature’, ‘explicature’, ‘unarticulated constituents’, ‘default heuristics’, ‘impliciture’. In this study we first take a sample of such phenomena and, from the point of view of pure pragmatics, survey the central descriptions of the content expressed and the mechanisms that might deliver these contents. We then, from the point of view of experimental pragmatics, focus on two accounts: Levinson’s I-heuristic, and Bach’s standardization. We find experimental evidence for the existence of such implicitures, and for the use of language specific standardizations over language neutral background ­information.

Robert M. Harnish

Papers

Minds, Brains, Computers: An Historical Introduction to the Foundations of Cognitive Science

Meaning and speech acts: A review of: Jerold J. Katz, Propositional structure and illocutionary force☆

Basic Topics in the Philosophy of Language

Linguistics : an introduction to language

Experimental pragmatics: Testing for implicitures