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Journal articles on the topic 'CLASSIFICATION OF RESEARCH'

Author: Grafiati

Published: 11 September 2023

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1

Kalandarova, Dilafruz Abdujamilovna. "RESEARCH CLASSIFICATION OF KARL REICHL." CURRENT RESEARCH JOURNAL OF PHILOLOGICAL SCIENCES 02, no. 11 (November 1, 2021): 29–33. http://dx.doi.org/10.37547/philological-crjps-02-11-08.

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The article discusses the fact that the famous German scientist Karl Reichl is a modern researcher, a classification of research created in the framework of his work in the process of recording and studying scientific, scientific-pedagogical, folklore and ethnographic materials.

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DeMers, Michael N. "Land Classification Research." International Journal of Applied Geospatial Research 5, no. 3 (July 2014): 82–92. http://dx.doi.org/10.4018/ijagr.2014070106.

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Land classification is so central to geography that its use, and the use of its derivative and corresponding products, is seldom even questioned. Since its earliest implementations land classification has adapted to changes in geographic scale and in the nature of the categorical systematics upon which it is based. Land classification has changed in its techniques and in how it adapts to technological changes, particularly those related to remote sensing and geographic information systems. The adaptation of land classification to digital pixel-based classification spawned a wide range of land classification error analysis techniques. These techniques do not easily transfer to non-pixel based classification error analysis as recent research on rapid land assessment methodologies and land change error analysis has shown. This disparity suggests a need to reevaluate the very nature of land classification research. To begin such an evaluation, this lecture provides a retrospective on the roots of land classification research, examines some of the milestones of that research, and describes the divergent paths such research has taken. It examines the importance of land classification in these times of ever decreasing global resources, and reviews its potential legal, social, and economic implications. Based on this retrospective, this paper advances the need for geographic researchers to envision land classification not only as a set of techniques, but more generally to focus on systematic geography in all its facets as a research agenda in its own right.

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Gracheva, R. G. "Genetic and substantive soil classifications and their applicability in geobotanical research." Plant Biology and Horticulture: theory, innovation, no. 149 (December 10, 2019): 55–64. http://dx.doi.org/10.36305/0201-7997-2019-149-55-64.

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The first soil classifications dividing soils according to their individual properties and reflecting the resource value of soils were, in fact, substantive classifications. The appearance of soil science that studies soils as special natural bodies changing in space according to natural laws has led to the emergence of genetic soil classifications. The most detailed multilevel Classification of soils of the USSR (1977) is ecological genetic classification based on the relationship between soils and soil formation conditions, where the type of vegetation cover is the top hierarchy level. With the development of soil science, inconsistencies between the genetic classification and the growing volume of knowledge, difficulties in its formalization, and contradictions with the zonal approach have appeared. A paradigm of substantive classification has emerged based on modern measurable morphological and analytical indicators of soils. It is most fully implemented in the World reference base for soil resources (WRB); a compromise option is the substantive- genetic Soil Classification of Russia (2004). In these classifications, for the first time soils with strong human influence (Antrosols and Technosols) are included at a high classification level. The tasks of substantive classifications are the certainty of soil identification; however, discrete classification cells with tight boundaries do not correspond to the continual nature of soils. When using WRB 2014, geobotanical studies receive a certain “freedom” from soils, at the same time, the rigid framework of the parameters of soil properties makes it possible to more accurately identify soil-depended condition of the vegetation cover.

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Ilic, Marina. "Sociometric research in pedagogy." Zbornik Instituta za pedagoska istrazivanja 45, no. 1 (2013): 24–41. http://dx.doi.org/10.2298/zipi1301024i.

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The use of sociometric research in pedagogy implies consideration of methodological demands pertaining to the selection and formulation of sociometric criteria, the selection of sociometric techniques, instruments and sociometric group classification methods. Various authors agree that sociometric criteria are basic relevant activities of the group they refer to and that they should be meaningful, understandable to every group member and clearly and precisely formulated. The selection of the appropriate sociometric technique and instrument has to be adjusted with the goal of sociometric research, along with the consideration of the advantages and short?comings of different sociometric techniques and instruments. The existing research provides very divergent results on the adequacy of different classification methods in identifying stable sociometric groups. Still, the majority of studies have confirmed that the two-dimensional rating scale method yields more stable classifications than the classification methods based on peer nominations, as well as that it is methodologically more justifiable to use cluster analysis in identifying stable sociometric status groups than the traditional classification methods.

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Vizine-Goetz, Diane. "Classification Research at OCLC." Journal of Library Administration 34, no. 1-2 (December 2001): 199–208. http://dx.doi.org/10.1300/j111v34n01_30.

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6

Berenbaum, Howard. "Classification and psychopathology research." Journal of Abnormal Psychology 122, no. 3 (2013): 894–901. http://dx.doi.org/10.1037/a0033096.

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7

Weihs, Claus, Uwe Ligges, Fabian Mörchen, and Daniel Müllensiefen. "Classification in music research." Advances in Data Analysis and Classification 1, no. 3 (November 21, 2007): 255–91. http://dx.doi.org/10.1007/s11634-007-0016-x.

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8

Feinstein, Alvan R. "Classification of epidemiologic research." Journal of Clinical Epidemiology 41, no. 8 (January 1988): 805. http://dx.doi.org/10.1016/0895-4356(88)90168-0.

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9

Abramson, J. "Classification of epidemiologic research." Journal of Clinical Epidemiology 42, no. 9 (1989): 819–20. http://dx.doi.org/10.1016/0895-4356(89)90091-7.

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10

Olesen, Jes. "The Future of Headache Classification and Classification Research." Cephalalgia 29, no. 12 (December 2009): 1240–41. http://dx.doi.org/10.1111/j.1468-2982.2009.01936.x.

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11

Cree, Ian A., and B. Iciar Indave. "Commentary: Cancer research quality and tumour classification." Tumor Biology 42, no. 2 (February 2020): 101042832090754. http://dx.doi.org/10.1177/1010428320907544.

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Cancer researchers require accurate diagnoses for the samples, cell lines, patients or populations that they study. These diagnoses are underpinned by an internationally accepted taxonomy – the World Health Organization Classification of Tumours. This is still largely based on the histopathological examination of biopsy specimens, but increasingly also molecular methods and radiological examination of patients. Classifications evolve as new evidence arises, and for tumours that evidence is available in a quantity that is both remarkable and daunting. Evaluating this deluge of new information and incorporating it into the World Health Organization Classification of Tumours is now the responsibility of an editorial board, and up to 200 editors and authors work on each system to update it within the new 5th edition. Just as cancer researchers depend on the classification for diagnoses, so too the classification depends on the generation of high-quality, trustworthy data by cancer researchers. It is not just a case of quantity but quality too. Scientific fraud is thankfully rare, but high-profile cases are damaging and standards need to improve, not least to ensure that accurate information enters the classification.

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P., Avila Clemenshia. "A Research on Cancer Subtype Classification Using Gene Expression Data." Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (March 31, 2020): 490–500. http://dx.doi.org/10.5373/jardcs/v12sp4/20201514.

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13

SATO, Yoshimichi. "Research Trends: Classification & Stratification." Japanese Sociological Review 59, no. 2 (2008): 388–404. http://dx.doi.org/10.4057/jsr.59.388.

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14

Fonn, Desmond. "FDA Research and Classification Initiatives." Eye & Contact Lens: Science & Clinical Practice 38, no. 6 (November 2012): 345. http://dx.doi.org/10.1097/icl.0b013e3182784621.

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15

Conway, Martha O'Hara. "Research in Cataloging and Classification:." Cataloging & Classification Quarterly 19, no. 1 (August 26, 1994): 119–29. http://dx.doi.org/10.1300/j104v19n01_09.

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Wang, Shi Min, and Xian Zhe Cao. "Research on Text Classification Technique." Applied Mechanics and Materials 278-280 (January 2013): 2081–84. http://dx.doi.org/10.4028/www.scientific.net/amm.278-280.2081.

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At first the paper will introduce the basic conception and the generic progress of text classification, after that it will introduce three text classification algorithms in detail and finally it will verify NB, SVM and KNN by experiment with the data mining software-weka. The result of experiment shows that KNN is more efficient than the other two algorithms in recall and precision.

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Ji, Shujuan, and Xiaohong Bao. "Research on Software Hazard Classification." Procedia Engineering 80 (2014): 407–14. http://dx.doi.org/10.1016/j.proeng.2014.09.098.

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18

Caramazza, Alfonso, and William Badecker. "Patient classification in neuropsychological research." Brain and Cognition 10, no. 2 (July 1989): 256–95. http://dx.doi.org/10.1016/0278-2626(89)90056-0.

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DeVita, Vincent T. "On the Classification of Research." Cancer Journal 9, no. 1 (January 2003): 2–3. http://dx.doi.org/10.1097/00130404-200301000-00002.

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DeVita, Vincent T. "On the classification of research." Current Oncology Reports 4, no. 6 (December 2002): 453–54. http://dx.doi.org/10.1007/s11912-002-0054-7.

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21

Martinić, Ivan. "An overview of classifications and modern research of springs in the world and in Croatia." Hrvatski geografski glasnik/Croatian Geographical Bulletin 84, no. 1 (July 14, 2022): 31–68. http://dx.doi.org/10.21861/hgg.2022.84.01.02.

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Because of their importance, water springs have been the subject of scholarly study since the late 17th century. With the development of science in the 19th, and especially in the 20th and 21st centuries, they became the object of study of various fields and branches of science. The result of the diverse studies of springs is a rich knowledge of their properties and the development of numerous classifications. Most basic classifications were created at the beginning of the 20th century, but a need for interdisciplinary research and classification of springs has emerged in the last twenty years. Today, however, there is no internationally-accepted interdisciplinary classification or database of springs that would facilitate the comparison of researched features and the use of research data. In Croatia, water-rich karst springs, which are attractive phenomena, have been thoroughly researched and classified, while non-karst springs have been examined less. The aim of this paper is to present the development of spring classifications throughout history, to emphasize the variety of research approaches that have been used, and to present the most important modern research of springs in the world and in Croatia.

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22

Jürgenmeyer, Julian, and Karoline Krenn. "Classification Situations : A New Field of Research for Valuation Studies?" Valuation Studies 4, no. 2 (December 20, 2016): 177–89. http://dx.doi.org/10.3384/vs.2001-5992.1642177.

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This conference note adds to recent discussions about the sociological implications of the spread of digital techniques for classifying market actors, specifically with regard to processes of social stratification. We first present some of the contributions to the conference “Classification Situations in Markets” and then discuss their implications for future research in general and the field of valuation studies in particular. We suggest three themes related to the conference that deserve further attention by students of valuation and related social processes: (a) the challenges posed by the rise of big data and algorithmic classifications to the study of classification and valuation; (b) the feedback loops of valuation regimes, in particular their consequences for conceptions of the self; and (c) the relation between classification situations and larger institutional settings, which implies a more explicitly comparative orientation.

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23

Zheng, Zhe, and Ren Jie Zhang. "Classification Research of Rotation Speed Measurement." Applied Mechanics and Materials 457-458 (October 2013): 998–1003. http://dx.doi.org/10.4028/www.scientific.net/amm.457-458.998.

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Instantaneous speed measurement system can accurately measure the rotating target and realize the rotation direction judgment. It plays a very important role in industrial applications, as monitoring or controlling the rotation speed of transmitter, motor, turbine. With the development of technology, a modern speed measurement system is demanded to be intelligent, high integration, high precision, high reliability, and low cost etc. Based on the large number of literatures, this paper is a comprehensive review of the principle and application of domestic and foreign mature technology of rotation speed measurement method at present. Through the systematic classification, relationship between the different speed measurements is revealed, and the advantages and disadvantages of different measuring sensor are presented, so that readers can choose reasonable speed measurement method depending on the application, the actual measurement requirements and the test purposes.

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24

Wilson, John M. "Classification of Models in Operational Research." Journal of the Operational Research Society 36, no. 3 (March 1985): 253. http://dx.doi.org/10.2307/2582257.

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Ivanova, Elena. "Classification of samples for comparative research." Vestnik of the St. Petersburg University of the Ministry of Internal Affairs of Russia 2019, no. 4 (December 25, 2019): 153–57. http://dx.doi.org/10.35750/2071-8284-2019-4-153-157.

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The article presents the classification of samples for comparative research, the study of which will contribute to the correct choice of a tactical method of obtaining them. The material also contains an analysis of the scientific literature related to the classification and systematization of the category in question; alternative grounds for dividing into groups are proposed. Classifying comparative samples, the author focuses on certain problems that practitioners face. Namely: the possibility of obtaining samples for a comparative research before the initiation of a criminal case, the validity of the use of coercion to obtain them, as well as the possibility of using comparative material obtained in the framework of operational search activities for expert studies. In preparing the article, general scientific methods of empirical knowledge (comparison, description), general logical methods of analysis, synthesis, generalization, classification, as well as methods of system-structural analysis were used. The purpose of the study of the problems of obtaining samples is to provide a systematic classification of this category in order to improve the quality of crime investigation. The conclusions made in the article on issues related to obtaining comparative samples at the stage of pre-investigation check and the possibility of using samples obtained during operational search activities are logically presented in the proposed classification.

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Xu, Gui Xian. "Research on Uighur Web Pages Classification." Applied Mechanics and Materials 513-517 (February 2014): 657–62. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.657.

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Uighur web pages classification is meaningful for the Uighur information processing. In this paper, we propose a classification approach for Uighur web pages. It utilizes the combination of two methods to classify the Uighur web pages into the predefined classes. One is the classification method based on Column Navigator of web page. The other is the content classification method based on the classes feature dictionary. Based on the proposed approach, we design the classification system of Uighur web pages. The experimental results present that the system has better performance for Uighur web pages classification. It is useful and helpful for the construction of high-quality Uighur corpus, Uighur information retrieval as well as Uighur text mining.

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27

McIlwaine, Ia C. "Indexing and the Classification Research Group." Indexer: The International Journal of Indexing: Volume 23, Issue 4 23, no. 4 (October 1, 2003): 204–8. http://dx.doi.org/10.3828/indexer.2003.23.4.8.

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The article outlines the origins and achievements of the Classification Research Group over the past 50 years in the development of indexing principles and techniques from chain indexing and facet analysis to relational indexing and PRECIS.

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Aksyutina, Z. A. "Classification of hypotheses for pedagogical research." Science of the Person: Humanitarian Researches 15, no. 2 (2021): 91–99. http://dx.doi.org/10.17238/issn1998-5320.2021.15.2.11.

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Ren, Xun Yi, and Wu Yuan. "Research on Privacy Preserving Classification Algorithm." Applied Mechanics and Materials 713-715 (January 2015): 1863–67. http://dx.doi.org/10.4028/www.scientific.net/amm.713-715.1863.

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In the process of data mining, how to operate the data mining as well as protect the private data is a problem must be solved. This paper proposed an improvement of decision tree classification algorithm. Homomorphism encryption system, digital envelopes technology and secret sorting are applied to protect the data privacy. Our contribution is a privacy preserving protocol consist of homomorphism encryption system and secret sorting. Analysis shows that this algorithm can get right results as well as protect the privacy of the private data.

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30

Wilson, John M. "Classification of Models in Operational Research." Journal of the Operational Research Society 36, no. 3 (March 1985): 253–56. http://dx.doi.org/10.1057/jors.1985.44.

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31

Jalote, Pankaj, Bijendra Nath Jain, and Sudhir Sopory. "Classification for research universities in India." Higher Education 79, no. 2 (June 3, 2019): 225–41. http://dx.doi.org/10.1007/s10734-019-00406-3.

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Greenland, Sander, and Hal Morgenstern. "Classification schemes for epidemiologic research designs." Journal of Clinical Epidemiology 41, no. 8 (January 1988): 715–16. http://dx.doi.org/10.1016/0895-4356(88)90155-2.

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33

Razzak, Mina. "New classification criteria developed for research." Nature Reviews Rheumatology 13, no. 12 (November 21, 2017): 694. http://dx.doi.org/10.1038/nrrheum.2017.192.

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34

Serhiyevich., Т. "CLASSIFICATION OF ROBOTS FOR ECONOMIC RESEARCH." Экономическая наука сегодня, no. 16 (September 15, 2022): 127–40. http://dx.doi.org/10.21122/2309-6667-2022-16-127-140.

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The article proposes criteria for distinguishing between different types of robots (field of application, type of working shell, specificity of replaced functions, level of autonomy and degree of mobility). We developed a classification of robots for the purposes of economic research. The robots used in the modern economy are investigated. The discussion that has developed in the literature regarding the physical embodiment as an (optional) feature of a robot, as well as the relationship between a robot and artificial intelligence, is considered.

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35

SalerovaD, K., and A. SarsembayevA. "Қолданыстағы бейнелерді жіктеу әдістерін зерттеу." INTERNATIONAL JOURNAL OF INFORMATION AND COMMUNICATION TECHNOLOGIES, no. 6(6) (March 14, 2022): 100–104. http://dx.doi.org/10.54309/ijict.2022.2.6.012.

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Image classification is a topical issue in data science, which is applied in many areas. It is the process of getting classes of information from a multichannel bitmap using specific rules. There are three approaches to image classification - supervised, unsupervised and object- based. Support Vector Machines, Artificial Neural Network, Decision Tree, Convolutional Neural Network are the solution methods used for image classification. Choice of the classification approach depends on the way an analyst interacts with the computer. The whole process boils down to image stack acquisition, preprocessing and classification. Кескіндерді жіктеу – кҿптеген салаларда қолданылатын деректанудағы ҿзекті тақырып. Бҧл нақты ережелерді қолдана отырып, кҿп жолақты растрлық картадан ақпарат алу процесі. Кескінді жіктеудің ҥш ҽдісі бар: бақыланатын оқыту, бақылаусыз оқыту жҽне объектілік. Олардың қолданылуы классификация кезінде талдаушының компьютермен ҿзара ҽрекеттесуіне байланысты болады. Процестің барлығы кескіндер стегін алудан, алдын ала ҿңдеу мен жіктелуден тҧрады. Тірек векторлық машиналар, жасанды нейрондық желілер, шешім ағаштары, конволюциялық нейрондық желілер – кескіндерді жіктеу ҽдістері.

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36

Robins, David. "Advances in Classification Research, Volume 3: Proceedings of the 3rd ASIS SIG/CR Classification Research Workshop." Information Processing & Management 31, no. 1 (January 1995): 148–49. http://dx.doi.org/10.1016/0306-4573(95)80023-m.

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37

Qiang\ WANG Zheng Zhi, LI. "Research on Remote Sensing Image Classification and Post Classification Integrated Techniques." National Remote Sensing Bulletin, no. 3 (1999): 193–98. http://dx.doi.org/10.11834/jrs.19990306.

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38

Sylenok, K. P. "The place of historical and archaeological research in the system of forensic examinations." Bulletin of Kharkiv National University of Internal Affairs 95, no. 4 (December 24, 2021): 219–29. http://dx.doi.org/10.32631/v.2021.4.19.

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The theory of forensic science has long been supplemented by additional classifications, which differed in certain criteria and comprehensively reflected the essence of forensic science and expert research. The issue of forensic examinations classification in Ukraine according to the criteria existing in the doctrine and normative legal acts has been considered. The purpose of the study is the correct classification of historical and archaeological examination in the system of forensic science of Ukraine, to determine the main aspects and criteria for the classification of forensic science in Ukraine. To solve this goal, the scientific literature and regulatory support on the topic have been analyzed. It has been concluded that the historical and archaeological examination does not belong to the already existing types, as it has a unique subject, object and methods. It can be classified according to various criteria presented in the article. Certain changes and transformations in the classifications of forensic examinations occur as a result of the emergence of a new type or kind of forensic examination, new objects and tasks of research. The development of a particular type of expertise can contribute to the formation of a new class. The most common is classification according to the field of special knowledge by types and subtypes, enshrined in the Instruction on the appointment and conduct of forensic examinations and expert research, approved by the Order of the Ministry of Justice of Ukraine. Historical and archaeological examination as a new type, which is currently in the process of formation and development, requires proper classification in expertise. Classifications are diverse depending on the criteria and have not only theoretical but also practical significance, allow to avoid errors in the appointment of forensic examinations and expert research, which, in turn, speeds up the study.

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Pokora, Roman Michael, Lucian Le Cornet, Philipp Daumke, Peter Mildenberger, Hajo Zeeb, and Maria Blettner. "Validation of Semantic Analyses of Unstructured Medical Data for Research Purposes." Das Gesundheitswesen 82, S 02 (October 9, 2019): S158—S164. http://dx.doi.org/10.1055/a-1007-8540.

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Abstract Background In secondary data there are often unstructured free texts. The aim of this study was to validate a text mining system to extract unstructured medical data for research purposes. Methods From a radiological department, 1,000 out of 7,102 CT findings were randomly selected. These were manually divided into defined groups by 2 physicians. For automated tagging and reporting, the text analysis software Averbis Extraction Platform (AEP) was used. Special features of the system are a morphological analysis for the decomposition of compound words as well as the recognition of noun phrases, abbreviations and negated statements. Based on the extracted standardized keywords, findings reports were assigned to the given findings groups using machine learning methods. To assess the reliability and validity of the automated process, the automated and two independent manual mappings were compared for matches in multiple runs. Results Manual classification was too time-consuming. In the case of automated keywording, the classification according to ICD-10 turned out to be unsuitable for our data. It also showed that the keyword search does not deliver reliable results. Computer-aided text mining and machine learning resulted in reliable results. The inter-rater reliability of the two manual classifications, as well as the machine and manual classification was very high. Both manual classifications were consistent in 93% of all findings. The kappa coefficient is 0.89 [95% confidence interval (CI) 0.87–0.92]. The automatic classification agreed with the independent, second manual classification in 86% of all findings (Kappa coefficient 0.79 [95% CI 0.75–0.81]). Discussion The classification of the software AEP was very good. In our study, however, it followed a systematic pattern. Most misclassifications were found in findings that indicate an increased risk of cancer. The free-text structure of the findings raises concerns about the feasibility of a purely automated analysis. The combination of human intellect and intelligent, adaptive software appears most suitable for mining unstructured but important textual information for research.

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40

de Leval, Laurence, and Elaine S. Jaffe. "Lymphoma Classification." Cancer Journal 26, no. 3 (2020): 176–85. http://dx.doi.org/10.1097/ppo.0000000000000451.

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Sobin, Leslie H., and Frederick L. Greene. "TNM classification." Cancer 92, no. 2 (2001): 452. http://dx.doi.org/10.1002/1097-0142(20010715)92:2<452::aid-cncr1342>3.0.co;2-b.

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AKCOLTEKIN, Alpturk, Ali Osman ENGIN, and Hikmet SEVGIN. "Attitudes of High School Teachers to Educational Research Using Classification‐ Tree Method." Eurasian Journal of Educational Research 17, no. 68 (April 27, 2017): 19–47. http://dx.doi.org/10.14689/ejer.2017.68.2.

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Peregorodiev, Ivan, Vagan Bokhian, Vera Delektorskaya, and Anna Safonova. "Modern view on the classification of neuroendocrine neoplasms of stomach." Problems in oncology 67, no. 5 (October 29, 2021): 600–605. http://dx.doi.org/10.37469/0507-3758-2021-67-5-600-605.

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Neuroendocrine neoplasms (NEN) of the stomach include a heterogeneous group of neoplasms characterized by different pathogenetic mechanisms, metastatic potential, and prognosis. The incidence of gastric NAN has increased 15-fold over the past few decades. It is necessary to clearly distinguish between well-differentiated neuroendocrine neoplasms (NETs) of the stomach and neuroendocrine cancer (NECs) of the stomach, as well as mixed neuroendocrine-non-neuroendocrine neoplasms. To date, there are three classifications that are used in clinical practice: clinicopathological classification, WHO classification, and UICC TNM classification. It should be noted that over the past five years, these classifications have undergone important changes. In the clinicopathological classification, in addition to the existing three types, a number of authors proposed to add the 4th type (developing as a result of inadequate functioning of the proton pump) and the 5th type (developing in patients who receive long-term therapy with proton pump inhibitors). In the 2019 WHO classification, an important change is the acceptance that well-differentiated NETs can have a high degree of malignancy – Grade III. The main changes in the 8th edition of the TNM classification concerned the differentiation of the M symbol. Thus, to date, the classification of gastric NENs allows us to accurately characterize tumor staging, the pathogenesis underlying their development, and prognosis. They form the basis for choosing an adequate treatment strategy that makes possible to achieve the most favourable result.

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Uprichard, Emma. "Dirty Data: Longitudinal Classification Systems." Sociological Review 59, no. 2_suppl (December 2011): 93–112. http://dx.doi.org/10.1111/j.1467-954x.2012.02058.x.

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Typically in longitudinal quantitative research, classifications are tracked over time. However, most classifications change in absolute terms in that some die whilst others are created, and in their meaning. There is a need, therefore, to re-think how longitudinal quantitative research might explore both the qualitative changes to classification systems as well as the quantitative changes within each classification. By drawing on the changing classifications of local food retail outlets in the city of York (UK) since the 1950s as an illustrative example, an alternative way of graphing longitudinal quantitative data is presented which ultimately provides a description of both types of change over time. In so doing, this article argues for the increased use of ‘dirty data’ in longitudinal quantitative analysis, a step which allows for the exploration of both qualitative and quantitative changes to, and within, classification systems. This ultimately challenges existing assumptions relating to the quality and type of data used in quantitative research and how change in the social world is measured in general.

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SHAO, Hua. "Research Design, Measurement, Classification and Future Research Directions of Coping." Advances in Psychological Science 20, no. 7 (July 2, 2013): 1121–27. http://dx.doi.org/10.3724/sp.j.1042.2012.01121.

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Sjögårde, Peter, and Per Ahlgren. "Granularity of algorithmically constructed publication-level classifications of research publications: Identification of specialties." Quantitative Science Studies 1, no. 1 (February 2020): 207–38. http://dx.doi.org/10.1162/qss_a_00004.

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In this work, we build on and use the outcome of an earlier study on topic identification in an algorithmically constructed publication-level classification (ACPLC), and address the issue of how to algorithmically obtain a classification of topics (containing articles), where the classes of the classification correspond to specialties. The methodology we propose, which is similar to that used in the earlier study, uses journals and their articles to construct a baseline classification. The underlying assumption of our approach is that journals of a particular size and focus have a scope that corresponds to specialties. By measuring the similarity between (1) the baseline classification and (2) multiple classifications obtained by topic clustering and using different values of a resolution parameter, we have identified a best performing ACPLC. In two case studies, we could identify the subject foci of the specialties involved, and the subject foci of specialties were relatively easy to distinguish. Further, the class size variation regarding the best performing ACPLC is moderate, and only a small proportion of the articles belong to very small classes. For these reasons, we conclude that the proposed methodology is suitable for determining the specialty granularity level of an ACPLC.

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Szostak, Rick. "Advances in Classification Research Online 2013 Classification, Ontology, and the Semantic Web." Advances in Classification Research Online 24, no. 1 (January 9, 2014): 30. http://dx.doi.org/10.7152/acro.v24i1.14674.

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The Semantic Web is developing slowly, but arguably surely. Two inter-related sources of delay are network effects and ontologies. The Semantic Web has come over time to rely onformal ontologies but there are many of these and they are each hard to master. The ability to link databases is compromised by the use of incompatible ontologies. But the RDF triplet format at the centre of the Semantic Web insists only on triplets of the form (object) (predicate orproperty) (subject). This paper explores the potential for a classification system that contains these three types of hierarchies (things, predicates, properties), plus a minimal set of rules on how they can be combined, to serve the needsof the Semantic Web. To this end, it surveys theroles (both the intended roles and side-effects) that formal ontologies play within the Semantic Web. The paper also briefly reviews the challenges faced in applying existing classification systems or thesauri to the Semantic Web.<br />

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Zhang, Xiaohua, Hui Wang, Wenxiang Xue, Chaoyun Qin, Yuping Wu, Shuyuan Wang, and Peng Qiu. "Research on classification method based on multi-scale segmentation and hierarchical classification." Journal of Physics: Conference Series 2189, no. 1 (February 1, 2022): 012029. http://dx.doi.org/10.1088/1742-6596/2189/1/012029.

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Abstract This paper transmission line corridors covering area in Hebei north area as the research object to explore multi-scale segmentation threshold suitable for Hebei north image, found applicable to Hebei north region segmentation threshold rules. Main methods are the object-oriented multi-scale segmentation and hierarchical classification, using image segmentation principle, make full use of high resolution image rich features such as shape, texture, object relationships. It is used for the follow-up investigation of hidden danger of external damage of power transmission channel in northern Hebei region. The main conclusions of the experiment are as follows: 1. By comparing and analyzing the results of five groups of different thresholds (40, 50, 60, 70 and 80), it is concluded that the single threshold of the multi-scale segmentation method suitable for most mountainous images in the study area is 60, and this method can achieve high-precision ground object classification for images in northern Hebei region. 2. The ground feature cover classification method that is more suitable for the parallel processing of a large number of study areas is the object-oriented hierarchical classification method, and preliminary exploration results of detailed parameters have been achieved.

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Miley, Frances, and Andrew Read. "The purgatorial shadows of war: Accounting, blame and shell shock pensions, 1914–1923." Accounting History 22, no. 1 (October 13, 2016): 5–28. http://dx.doi.org/10.1177/1032373216656648.

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This research analyses the role of accounting in British disablement pensions awarded to men who sustained shell shock during their Army service in the First World War. In this context, “accounting” refers to the classification of medical conditions when determining pension eligibility and awards. Pension classifications were prejudiced towards men with physical disabilities and against men with shell shock. Accounting classification’s ability to make a medical condition invisible is central to this research. The invisibility of shell shock as a medical condition in the pension classification system led to financial discrimination against men with shell shock. The immorality of this discrimination was hidden by a system of accounting classification that distanced decision-makers from the ramifications of their decisions. In addition, the Minister of Pensions adopted blame avoidance techniques to protect his Ministry and the British government from criticism occasioned by the discriminatory treatment of men with shell shock.

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Supe, Liāna, and Ingūna Jurgelāne-Kaldava. "Classification of higher education institutions: qualitative content analysis." Pedagoģija: teorija un prakse : zinātnisko rakstu krājums = Pedagogy: Theory and Practice : collection of scientific articles, no. IX (April 6, 2020): 87–99. http://dx.doi.org/10.37384/ptp.2020.09.087.

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Aim – to identify criterions and parameters for classification of higher education institutions, using research method – qualitative content analysis. The following tasks are defined for reaching the aim: to conduct qualitative content analysis and define categories and their frequency; to describe and analyse defined categories; to compare different classifications of higher education institutions; to summarize the analysis results and draw conclusions. Research methodology – overview of literature and qualitative content analysis. Findings – qualitative content analysis helps to structure the information gathered, to select the relevant and applicable, and leads to the development of new categories; higher education institutions are classified in many ways, using different criterions and parameters; classifications made by individual researchers are available, as well as universally known and applied classifications of higher education institutions such as Carnegie Classification and European classification of higher education institutions. Research limitations – the classification of higher education institutions is analysed only from Europe and the United States of America.

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