Categorization Process Research Articles

An Exploratory Sequential Mixed Methods Design to Identify Criteria for Continuous Performance Evaluation

An alternative to the ubiquitous annual performance evaluation is continuous performance evaluation that is posed to give more objective, frequent and constructive feedbacks. Despite researchers recommending following a continuous performance evaluation for employees, there are hardly any progress in this regard. One of the hindrances is in identifying the evaluation criteria. Identifying the relevant criteria – both from employers and employee’s perspective – is important for the success of a continuous performance evaluation process. Given its novelty, it calls for an exploration to understand a holistic set of criteria. For this purpose, an exploratory sequential mixed method was adopted in the Information Technology Industry in India. In the first phase, an unstructured interview was conducted among 11 software engineers (employees). Based on the responses, a semi-structured interview was conducted among 52 software engineers. A qualitative content analysis of the interview transcripts resulted in 33 criteria. Criteria that were similar, synonymous and complementary were combined. An acceptable interrater reliability was obtained in the content coding and categorization process (Krippendorff’s α = 0.822). This was followed by a focus group discussion among employers (represented by 15 project managers) and 11 criteria were eliminated and seven were added. In the second phase, the importance of 28 criteria were sought using a questionnaire based survey. A simple random sampling was adopted and 498 responses were received. (Cronbach’s α = 0.786). After elimination for incompleteness and erratic information, responses from 443 participants were considered for quantitative analysis. An exploratory factor analysis was conducted on the responses with 28 criteria subjected to exploratory factor analysis. Two criteria were not included for analysis due to a low mean sampling error value. The 26 most relevant criteria were categorized into six factors based on their factor loading. The main criteria factors were titled: Diagnostic, Dynamic, Proactive, Prompt, Resourceful and Responsible. These factors and the criteria within these factors can be used for continuously evaluating the performance of employees.

CATACHRESIS IN THE IMAGERY SPACE OF ENGLISH POETIC DRAMA: A COGNITIVE PERSPECTIVE

CATACHRESIS IN THE IMAGERY SPACE OF ENGLISH POETIC DRAMA: A COGNITIVE PERSPECTIVE

A theory of moral categorization: The conceptual performance of moral cognition

According to some traditional theories of categorization, human beings conceptually represent reality through judgments of similarity between the relevant features of some case, and the well-defined features of the concept that stands for the case. However, abstract concepts used with a moral sense do not have phenomenological resemblance with the cases they denote. In addition, concepts typically deployed in moral judgments are subject to semantic variation. Instead, this article proposes that moral categorization is based on an operational test that verifies whether certain impersonal (generic) and unconditional commands are observed or not in each specific case. The process of moral categorization is described using a cognitive procedure called "categorization by testing the command", consisting of five steps, namely: Descriptive stage, non-normative evaluation, normative question, conditional rule, and categorial construal. Finally, the phenomena of semantic polarity and semantic variation applied to the field of moral cognition are analyzed, to show how the proposed model can address the effects of these interpretive mechanisms.

No prescription? No problem: A qualitative study investigating self-medication with novel psychoactive substances (NPS).

The proliferation of Novel Psychoactive Substances (NPS) presents a challenge for global drug policy. The ease of online drug purchase and the emergence of the dark web have created new avenues for the growth of NPS. Despite the global nature of this issue, limited research has examined motivations of use. These include perceived safety or convenience, an interest in novel pharmacology and self-exploration. Recent evidence has suggested individuals may be self-medicating with NPS, however this phenomenon has yet to be thoroughly explored. This study aims to investigate the occurrence of NPS self-medication, identify the specific NPS involved, and understand the motivations behind their use. Discussions surrounding self-medication using NPS were collected between October 2022 and February 2023 via a content analysis of a Reddit community. Ninety-three threads, comprising 182,490 words and 5023 comments, were collected and cleaned. A frequency analysis was conducted to identify the NPS discussed, and data was analysed systematically through the process of iterative categorization (IC). Our study revealed frequent discussions about the self-medication with several NPS, notably etizolam, clonazolam, diclazepam, flualprazolam, 2-FMA, 4F-MPH, 3-FPM and 3-MeO-PCP. Individuals were mainly self-treating ADHD, anxiety and depression. Motivations for choosing NPS included access, cost, legality and a dissatisfaction with conventional healthcare. Substances were often chosen based on a profile of "Functionality" and outcomes varied. The use of clonazolam was highlighted as particularly problematic. The current study provides insight into the phenomenon of self-medication with NPS within an internet demographic, exploring the motivations behind why individuals choose NPS for a variety of disorders. The easy access to NPS and lack of scientific data pose a significant challenge for drug policy. Future policies should focus on improving healthcare providers knowledge of NPS use, removing barriers to adult ADHD diagnosis and rebuilding trust between individuals and addiction services.

How do young athletes experience selection and nonselection? An ethnographic study of talent categorization and identification processes.

How do young athletes experience selection and nonselection? An ethnographic study of talent categorization and identification processes.

Vulnerability assessment and categorization against heat waves for the Bilbao historic area

Climate change is threatening urban cultural heritage's preservation and survival. To improve the resilience of urban systems, planning decision-making processes must consider the risks that climate change poses to heritage. Nevertheless, despite the extensive body of research on climate-related hazards, there remains a significant knowledge deficiency related to the assessment of risks that considers heat waves and historic urban sites.This study's primary objective is to use Geographic Information Systems (GIS) data to analyse how historic urban locations and heat waves interact, as both urban systems and heritage areas. Socioeconomic, cultural, governmental, and physical aspects of the system are considered for a holistic approach. Key performance indicators, criteria, and requirements pertaining to key vulnerabilities of historic urban areas are identified to undertake a vulnerability assessment methodology, and appraised using the MIVES methodology, a Multi Criteria Decision Making Methodology (MCDM), to achieve this goal. Additionally, a reduced and data-constrained evaluation is provided through a categorization, for both buildings and public spaces. The categorization process and complete methodology are discussed along with the results of its application to a GIS-based model in Bilbao's historic district (Basque Country, Spain). This study seeks to be reproducible and be a tool for future global studies.

Enhancement of Diabetic Retinopathy Prognostication Using Deep Learning, CLAHE, and ESRGAN.

One of the primary causes of blindness in the diabetic population is diabetic retinopathy (DR). Many people could have their sight saved if only DR were detected and treated in time. Numerous Deep Learning (DL)-based methods have been presented to improve human analysis. Using a DL model with three scenarios, this research classified DR and its severity stages from fundus images using the "APTOS 2019 Blindness Detection" dataset. Following the adoption of the DL model, augmentation methods were implemented to generate a balanced dataset with consistent input parameters across all test scenarios. As a last step in the categorization process, the DenseNet-121 model was employed. Several methods, including Enhanced Super-resolution Generative Adversarial Networks (ESRGAN), Histogram Equalization (HIST), and Contrast Limited Adaptive HIST (CLAHE), have been used to enhance image quality in a variety of contexts. The suggested model detected the DR across all five APTOS 2019 grading process phases with the highest test accuracy of 98.36%, top-2 accuracy of 100%, and top-3 accuracy of 100%. Further evaluation criteria (precision, recall, and F1-score) for gauging the efficacy of the proposed model were established with the help of APTOS 2019. Furthermore, comparing CLAHE + ESRGAN against both state-of-the-art technology and other recommended methods, it was found that its use was more effective in DR classification.

ENGLISH NEOLOGISMS OF ANCIENT GREEK AND LATIN ORIGIN IN THE FIELD OF PSYCHOLOGY

In our rapidly developing world, people often need to create words to name new concepts. The process of creating a new word is actually very complex and consists of a number of stages that are mostly carried out at the subconscious level. People need to understand the role of a newly encountered phenomenon in the world around them and how that phenomenon will be classified within the framework of the already familiar categories. Another problem is the choice of the outer (phonetic) form of the newly created word, which is mainly based on the categorization process of the new phenomenon encountered by a person. This paper is aimed at studying the role of morphemes of Ancient Greek and Latin origin in the process of coining neologisms in the field of psychology. Psychology is a rapidly developing branch of science, and consequently new words need to be coined frequently. In the paper, we tried to reveal what kind of categorization processes take place when creating new words, and how these processes affect the external phonetic composition of given neologisms, and in particular, why Ancient Greek or Latin morphemes are chosen for coining neologisms in the field of psychology.

Comparison of the possibility of categorising proper names and common names in a disturbed discourse (in the course of mild cognitive impairment accompanying Parkinson’s disease)

The aim of the study is an assessment of the possibility of semantic categorisation of common names and proper names in a disturbed discourse. The study used a case-experimental approach. The results were analysed qualitatively and quantitatively. The best-preserved semantic relations were indicated and phenomena relating to the quality of access to the mental dictionary were determined. The symptoms of the degradation of the semantic dictionary were indicated. The quantitative assessment included a percentage description of the results obtained. The subject performed the process of semantic categorisation of proper names (names belonging to less fuzzy categories) better than common names (with numerous connotations). Keywords: proper names – common names – categorisation – MCI – semantic dictionary – disturbed discourse.

Evaluation of Performance Metrics in Heart Disease by Machine Learning Techniques

Aim: In addition to affecting the individual sociologically and psychologically, heart disease also poses important problems in health systems. Evaluation of heart disease performances has gained great importance in terms of machine learning method. In the study, performances were compared with the machine learning method for risk methods that classify heart illness.
 Materials and Methods: The categorization process Throughout the research made use of the "Heart Disease Dataset," an open access dataset. F1-score, sensitivity, selectivity, accuracy, balanced accuracy, negative and positive predictive values were used to assess the performance of the categorisation model using the machine learning approach. Random forest method, one of the variable selection methods, was used.
 Results: According to the relational classification model's classification findings for heart disease, the accuracy, balanced accuracy, sensitivity, selectivity, positive predictive value, negative predictive value, and F1-score values were observed to be 0.997, 0.997, 0.995, 1, 1, and 0.995, respectively. 
 Conclusion: The relational classification model proposed in the analysis obtained in the web-based open access dataset yielded distinctively successful results in classifying heart disease according to performance criteria.

Micro-Phenomenology as Experientially Based Access to Consciousness

Even though conscious experiences are explored in contemporary psychology with different methodological approaches significant questions remain: Can we explore first-person experiences with reliable methods? Why and in which circumstances should we trust first person reports? And how can we ensure that the process of verbalising mental experiences is not only a construction process but a methodological transformation, which enables intersubjective access to them. It is the aim of this paper to analyse how micro-phenomenology, understood as a simultaneously phenomenological and also empirical applied research method, tries to answer the questions and challenges mentioned above. To accomplish this, I focus in the first section on how firstand second-person access to conscious experiences is ensured and justified within the micro-phenomenological interview research procedure. In the second section, I concentrate on the question of whether micro-phenomenology has access to phenomenal experiences themselves given that they are mediated by interviews and complex processes of categorization and evaluation. In the final step, I analyse whether the explanatory scope of micro-phenomenology can be enhanced by integrating mixed method approaches to study mental phenomena qualitatively and quantitatively.

Diagnostic ability of deep learning in detection of pancreatic tumour

Pancreatic cancer is associated with higher mortality rates due to insufficient diagnosis techniques, often diagnosed at an advanced stage when effective treatment is no longer possible. Therefore, automated systems that can detect cancer early are crucial to improve diagnosis and treatment outcomes. In the medical field, several algorithms have been put into use. Valid and interpretable data are essential for effective diagnosis and therapy. There is much room for cutting-edge computer systems to develop. The main objective of this research is to predict pancreatic cancer early using deep learning and metaheuristic techniques. This research aims to create a deep learning and metaheuristic techniques-based system to predict pancreatic cancer early by analyzing medical imaging data, mainly CT scans, and identifying vital features and cancerous growths in the pancreas using Convolutional Neural Network (CNN) and YOLO model-based CNN (YCNN) models. Once diagnosed, the disease cannot be effectively treated, and its progression is unpredictable. That's why there's been a push in recent years to implement fully automated systems that can sense cancer at a prior stage and improve diagnosis and treatment. The paper aims to evaluate the effectiveness of the novel YCNN approach compared to other modern methods in predicting pancreatic cancer. To predict the vital features from the CT scan and the proportion of cancer feasts in the pancreas using the threshold parameters booked as markers. This paper employs a deep learning approach called a Convolutional Neural network (CNN) model to predict pancreatic cancer images. In addition, we use the YOLO model-based CNN (YCNN) to aid in the categorization process. Both biomarkers and CT image dataset is used for testing. The YCNN method was shown to perform well by a cent percent of accuracy compared to other modern techniques in a thorough review of comparative findings.

Semi-supervised learning models for document classification: A systematic review and meta-analysis

The continuous increase of digital documents on the web creates the need to search for information patterns that allow the categorization of organizational documents to generate knowledge in an institution. An Artificial Intelligence technique for this purpose is text classification, it for its application uses labels (previously categorized documents) with supervised (with labels) or unsupervised (without labels) training models. Both traditional models with their advantages and disadvantages have been joined into semi-supervised models that extract the best qualities of each one, however, the labeling process involves resources and time that try to be optimized to improve classification accuracy.
 An analysis of the different semi-supervised models would show us the advantages of their training and the way how the structure of each of them affects the accuracy of their classification. In the present study, a classification structure of the semi-supervised models in the classification of documents is proposed to analyze their qualities and categorization process, through an SLR (Revision of systematic literature) that extracts performance metrics from the identified studies to perform a meta-analysis through forest plots.
 To define the search strategy for studies, the PICOC (Population, Intervention, Comparison, Outputs, Context) method has been used, it is supported by the research question defines a search string, which has allowed the collection of 228 research, these are filtered with the PRISMA declaration method and the determination of exclusion criteria, in this way 35 researches are selected for the present study.
 The analysis of the selected studies identifies a structure for the different semi-supervised learning models, and a scheme of their work process is obtained, it has been used to extract advantages, disadvantages, and performance metrics. Through a meta-analysis with forest diagrams, the classification accuracy performance of the researches in each learning model is evaluated, determining as results that regardless of the characteristics of its process, active learning (0.89) and assembled learning (0.83) present the best performance levels.

Semi-supervised learning models for document classification: A systematic review and meta-analysis

The continuous increase of digital documents on the web creates the need to search for information patterns that allow the categorization of organizational documents to generate knowledge in an institution. An Artificial Intelligence technique for this purpose is text classification, it for its application uses labels (previously categorized documents) with supervised (with labels) or unsupervised (without labels) training models. Both traditional models with their advantages and disadvantages have been joined into semi-supervised models that extract the best qualities of each one, however, the labeling process involves resources and time that try to be optimized to improve classification accuracy.
 An analysis of the different semi-supervised models would show us the advantages of their training and the way how the structure of each of them affects the accuracy of their classification. In the present study, a classification structure of the semi-supervised models in the classification of documents is proposed to analyze their qualities and categorization process, through an SLR (Revision of systematic literature) that extracts performance metrics from the identified studies to perform a meta-analysis through forest plots.
 To define the search strategy for studies, the PICOC (Population, Intervention, Comparison, Outputs, Context) method has been used, it is supported by the research question defines a search string, which has allowed the collection of 228 research, these are filtered with the PRISMA declaration method and the determination of exclusion criteria, in this way 35 researches are selected for the present study.
 The analysis of the selected studies identifies a structure for the different semi-supervised learning models, and a scheme of their work process is obtained, it has been used to extract advantages, disadvantages, and performance metrics. Through a meta-analysis with forest diagrams, the classification accuracy performance of the researches in each learning model is evaluated, determining as results that regardless of the characteristics of its process, active learning (0.89) and assembled learning (0.83) present the best performance levels.

The Illusion of Identity: At-Home Ancestry Testing, Forensics, and the Accuracy Problem

Race, as a social construct, plays a significantly large role in the scientific field of genetics. This review analyzes several studies to reach a more comprehensive understanding of race’s historical influence in genetics, the modern implications of this relationship, and the validity of race as a scientific category in genetics. While the scientific validity of race has largely been disproved, it continues to be used by scientists and geneticists, specifically in genetic disease research. Aside from the obstacles stemming from race’s lack of legitimacy in science, its social implications are also a liability for scientific advancements because of how race has historically marginalized people groups. Moreover, the monetization of recent discoveries in genetics, including at-home ancestry test kits and molecular photofitting in forensics, are a cause for concern due to their lack of scientific accuracy. The process of racial categorization of test subjects is not as accurate as companies advertise, and this lack of transparency can lead to false conclusions about personal identity based on misinformation of at-home ancestry test results which rely upon a limited data set. In addition, this possibility of error in the DNA testing process can lead to the criminalization of innocent suspects. Therefore, awareness about both the primary motives of these companies, as well as the social implications of interpreting genetic test results, is critical to understanding the broader effects of race in genetics in society.

Single registered midwives contributing care for general patients: a scoping study

Background: Midwifery vacancies persist in small maternity units in Australian rural and private settings where midwives are expected to also care for general (non-maternity) patients when midwifery activity is low. Recruiting dual registered nurse/midwives over single registered midwives is preferred, as single registered midwives are seen as inflexible to assist with nursing work. Little is known about single registered midwives’ contribution to care of general patients in small maternity units. Objective: This scoping study aimed to consult single registered midwives and managers of single registered midwives to determine perspectives on single registered midwives’ contribution in small maternity units where the workload encompasses both midwifery and care of general patients, to inform further research. Study design and methods: This study used a qualitative description design. Two online focus groups were held, one containing three single registered midwives, the other three managers of single registered midwives. Similar questions were posed to each group about single registered midwives’ contribution to care in small maternity units. Data analysis was conducted collaboratively through coding and thematic categorisation processes. Results: Four major categories were found. Single registered midwives’ scope of practice concerning general patients is undefined; single registered midwives possess transferrable clinical skills applicable to general patients; practical, professional, and emotional barriers exist for single-registered midwives in small maternity units; and future research recommendations include scope of practice and workplace experiences. Discussion: Persistent midwifery vacancies are implicated in the closure of small maternity units to the detriment of childbearing families. Understanding the contribution of single registered midwives in small maternity units will inform future research and midwifery recruitment strategies to improve access to services. Conclusion: The experiences of single registered midwives working in small maternity units warrants further investigation. This scoping study contributes to the literature about single registered midwives’ experiences in small maternity units and suggests considerations for future research. Implications for research, policy and practice: Findings from this study provide information about the contribution of single registered midwives to small maternity units where there is an expectation to contribute to care of general patients in addition to midwifery. Future research into the experiences of single registered midwives working in these settings will generate information to inform recruitment strategies, potentially improving access to maternity care in small maternity units and may be used in the review of midwifery regulation and educational standards. What is already known about the topic? Midwifery vacancies persist in small maternity units in rural and regional areas where the workload comprises both midwifery and nursing practice, despite strategies to recruit dual registered nurse/midwives. Dual registrants face challenges in maintaining both nursing and midwifery professional obligations in small maternity units. There are increasing numbers of single registered midwives registering each year in Australia and educational opportunities to become a single registered midwife exist in rural settings. What this paper adds: Single registered midwives make a useful contribution to care in small maternity units, and they assist with the care of general patients. The scope of practice for single registered midwives in assisting with general patients is not defined. Recommendations for future research include scope of practice for assisting with general patients and workplace challenges faced by single registered midwives because of their midwife-only qualification.

Smart IoT in Breast Cancer Detection Using Optimal Deep Learning.

IoT in healthcare systems is currently a viable option for providing higher-quality medical care for contemporary e-healthcare. Using an Internet of Things (IoT)-based smart healthcare system, a trustworthy breast cancer classification method called Feedback Artificial Crow Search (FACS)-based Shepherd Convolutional Neural Network (ShCNN) is developed in this research. To choose the best routes, the secure routing operation is first carried out using the recommended FACS while taking fitness measures such as distance, energy, link quality, and latency into account. Then, by merging the Crow Search Algorithm (CSA) and Feedback Artificial Tree, the produced FACS is put into practice (FAT). After the completion of routing phase, the breast cancer categorization process is started at the base station. The feature extraction step is then introduced to the pre-processed input mammography image. As a result, it is possible to successfully get features including area, mean, variance, energy, contrast, correlation, skewness, homogeneity, Gray Level Co-occurrence Matrix (GLCM), and Local Gabor Binary Pattern (LGBP). The quality of the image is next enhanced through data augmentation, and finally, the developed FACS algorithm's ShCNN is used to classify breast cancer. The performance of FACS-based ShCNN is examined using six metrics, including energy, delay, accuracy, sensitivity, specificity, and True Positive Rate (TPR), with the maximum energy of 0.562J, the least delay of 0.452s, the highest accuracy of 91.56%, the higher sensitivity of 96.10%, the highest specificity of 91.80%, and the maximum TPR of 99.45%.

Rampant Abstraction as a Strategy of Singularization: Genre on Spotify

If processes of categorization are central to cultural sociology, then current developments as regards genre formation in the realm of music-streaming services call for attention. A double process of computerized genre analysis (producing a potentially infinite array of categories) and increasingly context-specific music recommendation (accommodating a vision of limitless personalization) challenges established, scene- and identity-based ideas about genre, as developed in popular-music studies. Drawing on Reckwitz’s (2020) theory of the society of singularities, this article argues for considering this double process as the intersection of the logics of, respectively, the general and the particular. These logics are mediated by a dynamic sense of abstraction, involved in processes of labelling, enabling levels of generality while manifesting a potential for concretion. The increased scope, acceleration, and dynamicity of such abstraction mark genre formation in digital times. The article makes this argument looking particularly at the case of Spotify – market leader and front runner in the noted developments – as a basis for engaging broader questions about musical genre theory in the context of digitized culture and current conditions of musical life.

Toward ecological environmental risk for spoil ground group management in mega projects

PurposeThe numerous spoil grounds brought about by mega transportation infrastructure projects which can be influenced by the ecological environment. To achieve better management of spoil grounds, this paper aims to assess their comprehensive risk levels and categorize them into different categories based on ecological environmental risks.Design/methodology/approachBased on analysis of the environmental characteristics of spoil grounds, this paper first comprehensively identified the ecological environmental risk factors and developed a risk assessment index system to quantitatively describe the comprehensive risk levels. Second, this paper proposed a comprehensive model to determine the risk assessment and categorization of spoil ground group in mega projects integrating improved projection pursuit clustering (PPC) method and K-means clustering algorithm. Finally, a case study of a spoil ground group (includes 50 spoil grounds) in a mega infrastructure project in western China is presented to demonstrate and validate the proposed method.FindingsThe results show that our proposed comprehensive model can efficiently assess and categorize the spoil grounds in the group based on their comprehensive ecological environmental risk. In addition, during the process of risk assessment and categorization of spoil grounds, it is necessary to distinguish between sensitive factors and nonsensitive factors. The differences between different categories of spoil grounds can be recognized based on nonsensitive factors, and high-risk spoil grounds which need to be focused more on can be identified according to sensitive factors.Originality/valueThis paper develops a comprehensive model of risk assessment and categorization of a group of spoil grounds based on their ecological environmental risks, which can provide a reference for the management of spoil grounds in mega projects.

Substitution policies as a categorization process. Making biological alternatives a solution for the French national pesticide reduction plan

This article focuses on the emergence and implementation of a specific policy option in a national plan aiming to reduce pesticide consumption: the substitution of biological substances for chemicals. I argue that substitution is a singular method of handling the removal of pesticides and analyze the political consequences of this approach. Empirically, this work is based on a three-year qualitative investigation (2015–2018) into the French policy plan for pesticide use reduction (Ecophyto). Drawing on the literature on categories and classifications, I show that making substitution a viable policy option relies primarily on categorization processes and on the establishment of clear distinctions. By shedding light on the specific type of administrative and technical intervention substitution as a policy option requires, the aim of this paper is to contribute to a better understanding of the political dimension of transitions.