Theory-driven statistical modeling for semantics and pragmatics: A case study on grammatically generated implicature readings

Language ◽  
2020 ◽  
Vol 96 (2) ◽  
pp. e77-e96
Author(s):  
Michael Franke ◽  
Leon Bergen
Keyword(s):  
Statistical Modeling ◽  
Semantics And Pragmatics

A properly pragmatist pragmatics

2020 ◽  
Vol 27 (2) ◽  
pp. 387-407
Author(s):  
Catherine Legg
Keyword(s):  
Philosophy Of Language ◽  
Separate Study ◽  
Semantics And Pragmatics ◽  
Pragmatist Philosophy

Abstract Although most contemporary philosophers of language hold that semantics and pragmatics require separate study, there is surprisingly little agreement on where exactly the line should be drawn between these two areas, and why. In this paper I suggest that this lack of clarity is at least partly caused by a certain historical obfuscation of the roots of the founding three-way distinction between syntax, semantics and pragmatics in Charles Peirce’s pragmatist philosophy of language. I then argue for recovering and revisiting these original roots, taking indexicality as a case-study of how certain questions connected with the distinction which are currently considered complex and difficult may be clarified by a ‘properly pragmatist pragmatics’. Such a view, I shall argue, upends a certain priority usually accorded to semantics over pragmatics, teaching that we do not work out what terms mean in some abstract overall sense and then work out to what use they are being put; rather, we must understand to what use terms are being put in order to understand what they mean.

scholarly journals Statistics in the Service of Science: Don't let the Tail Wag the Dog

2021 ◽  
Author(s):  
Henrik Singmann ◽  
Gregory Edward Cox ◽  
David Kellen ◽  
Suyog Chandramouli ◽  
Clintin Davis-Stober ◽  
...  
Keyword(s):  
Statistical Modeling ◽  
Statistical Models ◽  
Mixed Models ◽  
Model Comparison ◽  
Bayes Factors ◽  
Causal Mechanisms ◽  
Science Models ◽  
Scientific Goal ◽  
Coordination Function

Statistical modeling is generally meant to describe patterns in data in service of the broader scientific goal of developing theories to explain those patterns. Statistical models support meaningful inferences when models are built so as to align parameters of the model with potential causal mechanisms and how they manifest in data. When statistical models are instead based on assumptions chosen by default, Attempts to draw inferences can be uninformative or even paradoxical—in essence, the tail is trying to wag the dog.These issues are illustrated by van Doorn et al. (in press) in the context of using BayesFactors to identify effects and interactions in linear mixed models. We show that the problems identified in their applications can be circumvented by using priors over inherently meaningful units instead of default priors on standardized scales. This case study illustrates how researchers must directly engage with a number of substantive issues in order to support meaningful inferences, of which we highlight two: The first is the problem of coordination, which requires a researcher to specify how the theoretical constructs postulated by a model are functionally related to observable variables. The second is the problem of generalization, which requires a researcher to consider how a model may represent theoretical constructs shared across similar but non-identical situations, along with the fact that model comparison metrics like Bayes Factors do not directly address this form of generalization. For statistical modeling to serve the goals of science, models cannot be based on default assumptions, but should instead be based on an understanding of their coordination function and on how they represent causal mechanisms that may be expected to generalize to other related scenarios.

Statistical modeling the effect of sediment physicochemical properties on the concentration of heavy metal (case study: Musa Creek, SW Iran)

2018 ◽  
Vol 77 (3) ◽  
Author(s):  
Maryam Ghazanfari Shabankareh ◽  
Hakimeh Amanipoor ◽  
Sedigheh Battaleb-Looie ◽  
Javad Dravishi Khatooni
Keyword(s):  
Heavy Metal ◽  
Physicochemical Properties ◽  
Statistical Modeling ◽  
Metal Case ◽  
Sw Iran

Proper Statistical Modeling and Validation in QSAR: A Case Study in the Prediction of Rat Fat-Air Partitioning

2007 ◽  
Author(s):  
Subhash C. Basak ◽  
Denise Mills ◽  
Douglas M. Hawkins ◽  
Jessica J. Kraker ◽  
Theodore E. Simos ◽  
...  
Keyword(s):  
Statistical Modeling

scholarly journals FIRST LANGUAGE ACQUISITION OF A THREE YEARS OLD CHILD: AN ANALYSIS OF PHONOLOGICAL COMPONENT (A CASE STUDY)

2018 ◽  
Vol 4 (2) ◽  
pp. 150-154
Author(s):  
Hilma Safitri ◽  
M. Nur Hakim
Keyword(s):  
Language Acquisition ◽  
Qualitative Method ◽  
First Language ◽  
First Language Acquisition ◽  
Human Beings ◽  
Semantics And Pragmatics ◽  
Biological Development ◽  
Consonant Sounds ◽  
Do So

The process of language acquisition undergone by each child in the world is more and less similar. This is because language is universal in which it is acquired through all language components namely phonology, semantics, and pragmatics. The component of phonology is more related to human neuro-biology. The process of sound produced is genetic and human biological development is not similar. Hence, the language development of human beings is not exactly the same. This paper explores first language acquisition particularly on the phonological component of a three years old child named Andi. The data is the transcripts of dialog taken from causal chit chats with the participant. A qualitative method is used to analyze the data. The findings reveal that the participant acquired vocal sounds of /a/, /i/, /u/, /o/, /e/ and consonant sounds of /p/, /b/, /m/, /t/ more dominant compared to others. He never produced /k/ consonant, fricative [s] and [j]. However, he produced nasal consonants of [m], [n], and [ɳ]. The participant also substituted omitted a few sounds. This might happen because his speech articulation has not developed well yet or genetic factor does not allow him to do so.

scholarly journals Analysis and assessment of productivity in the parquet production by applying the methods of statistical modeling: Case study of selected production system in Serbia

2019 ◽  
pp. 37-56
Author(s):  
Miljan Kalem ◽  
Slobodanka Mitrovic ◽  
Aleksandra Lazarevic
Keyword(s):  
Statistical Modeling ◽  
Production System ◽  
Strong Influence ◽  
Statistical Significance ◽  
Statistical Program ◽  
Main Factors ◽  
The Republic ◽  
Factor Interaction ◽  
Factor Dimension

In this paper, researching results of the selected factors? influence on productivity of parquet production on the example of the selected company in the Republic of Serbia are presented. The analysis has included the influence of the two main factors, dimension and class of parquet quality on productivity. This research has had an aim to determine dependency between the productivity and the mentioned factors, to get corresponding conclusions, to give expert recommendation and to propose suitable management decisions, which would provide an increase in the productivity in the selected company. In order to determine the dependency between the productivity and the mentioned factors, statistical modeling is performed in the program SPSS v.20. Interpretation of the results in the statistical program SPSS, established a strong influence of the analyzed factors on productivity. Statistical significance of the factor dimension influence on productivity is Sig.1= 0.010, while the statistical significance of the parquet class quality factor influence on productivity is Sig. = 0,000. Statistical significance of the factor interaction influence of these factors on productivity is Sig. = 0,028. According to the researching results, it is concluded that parquet productivity depends on the mentioned factors.

As Others See Us: A Case Study in Path Analysis

1987 ◽  
Vol 12 (2) ◽  
pp. 101-128 ◽  
Author(s):  
D. A. Freedman
Keyword(s):  
United States ◽  
Comparative Study ◽  
Path Analysis ◽  
Statistical Modeling ◽  
Social Mobility ◽  
The United States ◽  
Path Model ◽  
Social Data ◽  
Complex Phenomena

In 1967, Blau and Duncan proposed a path model for education and stratification. This is one of the most influential applications of statistical modeling technique to social data. There is recent use of the same technique in Hope’s (1984) comparative study of Scotland and the United States, As Others See Us: Schooling and Social Mobility in Scotland and the United States. A review of path analysis is offered here, with Hope’s model used as an example, the object being to suggest the limits of the method in analyzing complex phenomena.

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