Analysing dynamics of crop problems by applying text analysis methods on farm advisory data of eSaguTM

Author(s):  
R. Uday Kiran ◽  
P. Krishna Reddy ◽  
M. Kumara Swamy ◽  
G. Syamasundar Reddy
2020 ◽  
Vol 6 (32) ◽  
pp. eaba2196
Author(s):  
Ryan L. Boyd ◽  
Kate G. Blackburn ◽  
James W. Pennebaker

Scholars across disciplines have long debated the existence of a common structure that underlies narratives. Using computer-based language analysis methods, several structural and psychological categories of language were measured across ~40,000 traditional narratives (e.g., novels and movie scripts) and ~20,000 nontraditional narratives (science reporting in newspaper articles, TED talks, and Supreme Court opinions). Across traditional narratives, a consistent underlying story structure emerged that revealed three primary processes: staging, plot progression, and cognitive tension. No evidence emerged to indicate that adherence to normative story structures was related to the popularity of the story. Last, analysis of fact-driven texts revealed structures that differed from story-based narratives.


1970 ◽  
Vol 6 ◽  
pp. 98-108
Author(s):  
Bal K Joshi ◽  
Madhusudan P Upadhyay ◽  
Hari P Bimb ◽  
D Gauchan ◽  
BK Baniya

Synthesizing data analysis methods adopted under in situ global project in Nepal along withvariables and nature of study could be guiding reference for researchers especially to those involvedin on farm research. The review work was conducted with the objective to help in utilizing andmanaging in situ database system. The objectives of the experiment, the structure of the treatmentsand the experimental design used primarily determine the type of analysis. There were 60 papers ofthis project published in Nepal. All these papers are grouped under 8 thematic groups namely 1.Agroecosystem (3 papers), 2. Agromorphological and farmers’ perception (7 papers), 3. Croppopulation structure (5 papers), 4. Gender, policy and general (15 papers), 5. Isozyme andmolecular (6 papers), 6. Seed systems and farmers’ networks (5 papers), 7. Social, cultural andeconomical (11 papers) and 8. Value addition (8 papers). All these papers were reviewed basicallyfor data type, sample size, sampling methods, statistical methods and tools, varieties and purposes.Descriptive and inferential statistics along with multivariate methods were commonly used in onfarm research. Experimental design, the most common in on station trial was least used. Study overspace and time was not adopted. There were 5 kinds of data generated, 45 statistical tools adoptedin eight different crop species. Among the 5 kinds of data under these eight subject areas,categorical type was highest followed by discrete numerical. Binary type was least in frequency.Most of the papers were related to rice followed by taro and finger millet. Cucumber and pigeonpea were studied least. Descriptive statistics along with Χ2, multivariate analysis and regressionapproaches would be appropriate tools. Similarly SPSS and MINITAB may be good software. Thebest one among a number of statistical tools should be selected and utmost care must be exercisedwhile collecting data.Key words: Data analysis methods; on farm research; on station research; subject areasDOI: 10.3126/narj.v6i0.3371Nepal Agriculture Research Journal Vol.6 2005 pp.98-108


Author(s):  
Or Haim Anidjar ◽  
Ishak Lapidot ◽  
Chen Hajaj ◽  
Amit Dvir ◽  
Gilad Issachar

2015 ◽  
Vol 67 (2) ◽  
pp. 203-229 ◽  
Author(s):  
Jacobus Philippus van Deventer ◽  
Cornelius Johannes Kruger ◽  
Roy Donald Johnson

Purpose – Academic authors tend to define terms that meet their own needs. Knowledge Management (KM) is a term that comes to mind and is examined in this study. Lexicographical research identified KM terms used by authors from 1996 to 2006 in academic outlets to define KM. Data were collected based on strict criteria which included that definitions should be unique instances. From 2006 onwards, these authors could not identify new unique instances of definitions with repetitive usage of such definition instances. Analysis revealed that KM is directly defined by People (Person and Organisation), Processes (Codify, Share, Leverage, and Process) and Contextualised Content (Information). The paper aims to discuss these issues. Design/methodology/approach – The aim of this paper is to add to the body of knowledge in the KM discipline and supply KM practitioners and scholars with insight into what is commonly regarded to be KM so as to reignite the debate on what one could consider as KM. The lexicon used by KM scholars was evaluated though the application of lexicographical research methods as extended though Knowledge Discovery and Text Analysis methods. Findings – By simplifying term relationships through the application of lexicographical research methods, as extended though Knowledge Discovery and Text Analysis methods, it was found that KM is directly defined by People (Person and Organisation), Processes (Codify, Share, Leverage, Process) and Contextualised Content (Information). One would therefore be able to indicate that KM, from an academic point of view, refers to people processing contextualised content. Research limitations/implications – In total, 42 definitions were identified spanning a period of 11 years. This represented the first use of KM through the estimated apex of terms used. From 2006 onwards definitions were used in repetition, and all definitions that were considered to repeat were therefore subsequently excluded as not being unique instances. All definitions listed are by no means complete and exhaustive. The definitions are viewed outside the scope and context in which they were originally formulated and then used to review the key concepts in the definitions themselves. Social implications – When the authors refer to the aforementioned discussion of KM content as well as the presentation of the method followed in this paper, the authors may have a few implications for future research in KM. First the research validates ideas presented by the OECD in 2005 pertaining to KM. It also validates that through the evolution of KM, the authors ended with a description of KM that may be seen as a standardised description. If the authors as academics and practitioners, for example, refer to KM as the same construct and/or idea, it has the potential to speculatively, distinguish between what KM may or may not be. Originality/value – By simplifying the term used to define KM, by focusing on the most common definitions, the paper assist in refocusing KM by reconsidering the dimensions that is the most common in how it has been defined over time. This would hopefully assist in reigniting discussions about KM and how it may be used to the benefit of an organisation.


1994 ◽  
Vol 5 (1) ◽  
pp. 62-80 ◽  
Author(s):  
Malcolm Rimmer ◽  
Lee Watts

This paper is a selective review of recent Australian research upon enterprise bargaining and workplace industrial relations. It begins with a discussion of data collection methods, pointing out some strengths and weaknesses of survey, case study, and agreement text analysis methods. It then focusses upon two substantive issues to test the success of research. The first concerns the infrastructure for enterprise bargaining. Our conclusion is that research illuminates this topic, and reveals general unreadiness. The second issue is productivity performance and enterprise bargaining. We are far more sceptical that research has proved a relation between the two. We conclude with the observation that researchers may be on the wrong track if they try to quantify the effects of enterprise bargaining on productivity performance. The paper recommends that greater attention be given to change management programmes designed to increase competitiveness, and to the outcomes sought from these, rather than to productivity.


2019 ◽  
Author(s):  
Martijn Schoonvelde ◽  
Christian Pipal ◽  
Gijs Schumacher

This chapter provides an assessment of automated text analysis methods in political psychology structured around the following core questions: (i) What is the current state of affairs of text as data in political psychology? (ii) What can political science and politicalpsychology learn from each other when it comes to analysing natural language? (iii) Wheredoes text as data in political psychology go next?


2018 ◽  
pp. 49-64
Author(s):  
Alena Suvorova ◽  
◽  
Karina Smirnova ◽  
Evgeniy Budin ◽  
Tatiana Tulupyeva ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document