Mapping of Applied Psychology Publications: A Study Based on Scimago Journal and Country Rank Database

This paper discusses about the Applied Psychology publications and its citation available in the Scimago Journal and Country Rank data base by the authors from top 15 countries (based on publications). The relevant data are collected from Scimago Journal and Country Rank data base and it was analyzed. Itshows among the Applied Psychology publications totally 99276 articles were published the maximum of 47753(48.10%) articles published by United States followed by United Kingdom with 11819(11.91%) publications during the study period

Scientometric Analysis of Indian Citation Index (2004-2015): Profile of the Domain of Psychology Journals

Indian Citation Index database is a powerful tool to search, track, and measure and collaborate in the sciences, social sciences, arts and humanities. This paper is discussed the published research articles, citations and self-citations in the Psychology journals which are available in Indian Citation Index. There are 3333 articles published from 6 Psychology Journals. Among the journals, Indian Journal of Psychiatry occupies first position with 989 (29.68%) articles and Indian Journal of Psychological Medicine occupies second rank with 644 (19.32%) articles and Psychological Studies is in third 584 (17.52%). Further, in the year 2012, 390 (11.70%) articles are published and it is highest when comparing to other years. It is pointed out that the frequency of the journals is varying and that is one of the reasons for the strength of articles

MEMPREDIKSI PENINGKATAN H-INDEKS UNTUK JURNAL PENELITIAN DENGAN MENGGUNAKAN ALGORITMA COST-SENSITIVE SELECTIVE NAIVE BAYES CLASSIFIERS

Machine learning community is not only interested in maximizing classification accuracy, but also in minimizing the distances between the actual and the predicted class. Some ideas, like the cost-sensitive learning approach, are proposed to face this problem. In this paper, we propose two greedy wrapper forward cost-sensitive selective naive Bayes approaches. Both approaches readjust the probability thresholds of each class to select the class with the minimum-expected cost. The first algorithm (CSSNB-Accuracy) considers adding each variable to the model and measures the performance of the resulting model on the training data. The variable that most improves the accuracy, that is, the percentage of well classified instances between the readjusted class and actual class, is permanently added to the model. In contrast, the second algorithm (CS-SNB-Cost) considers adding variables that reduce the misclassification cost, that is, the distance between the readjusted class and actual class. We have tested our algorithms on the bibliometric indices prediction area. Considering the popularity of the well-known h-index, we have researched and built several prediction models to forecast the annual increase of the h-index for Neurosciences journals in a four-year time horizon. Results show that our approaches, particularly CS-SNB-Accuracy, achieved higher accuracy values than the analyzed cost sensitive classifiers and Bayesian classifiers. Furthermore, we also noted that the CS-SNB-Cost always achieved a lower average cost than all analyzed cost-sensitive and cost-insensitive classifiers. These cost sensitive selective naive Bayes approaches outperform the selective naive Bayes in terms of accuracy and average cost, so the cost-sensitive learning approach could be also applied in different probabilistic classification approaches

Analyzing Business Research on the Foreign Corrupt Practices Act: Clusters, Gaps, and Future Directions

This study comprehensively analyzed and summarized business-related research on the Foreign Corrupt Practices Act (FCPA). Research on the FCPA is essential because sanctions for violations have grown substantially, increasing risks for multinational enterprises (MNEs). Recent fines exceeded $1 billion, and business executives were personally fined and imprisoned (Stanford Foreign Corrupt Practices Act Clearinghouse, 2021). Unfortunately, theory-based and empirically-validated business research has not kept pace with this increased risk. Performance mapping and science mapping pinpointed the most prolific academic fields, the most cited articles, and clusters of authors, journals, and keywords. Analyses identified gaps in the literature. Prior research focused on public policy questions, like the impact of the FCPA on American companies (Shapiro, 2013), the propriety of attempting to regulate foreign business ethics, and international treaties. Moreover, significant clustering and fractionalization into legal academic silos have side-stepped business-related research topics. New and different research directions are proposed

Genetic algorithms and Gaussian Bayesian networks to uncover the predictive core set of bibliometric indices

The diversity of bibliometric indices today poses the challenge of exploiting the relationships among them. Our research uncovers the best core set of relevant indices for predicting other bibliometric indices. An added difficulty is to select the role of each variable, that is, which bibliometric indices are predictive variables and which are response variables. This results in a novel multioutput regression problem where the role of each variable (predictor or response) is unknown beforehand. We use Gaussian Bayesian networks to solve the this problem and discover multivariate relationships among bibliometric indices. These networks are learnt by a genetic algorithm that looks for the optimal models that best predict bibliometric data. Results show that the optimal induced Gaussian Bayesian networks corroborate previous relationships between several indices, but also suggest new, previously unreported interactions. An extended analysis of the best model illustrates that a set of 12 bibliometric indices can be accurately predicted using only a smaller predictive core subset composed of citations, g-index, q2-index, and hr-index. This research is performed using bibliometric data on Spanish full professors associated with the computer science area

An examination of dose in mindfulness-based programs and Mindfulness practice through a dose-response meta-regression and randomised controlled experiments

Mindfulness research has grown exponentially in recent years including research with various doses related to mindfulness-based programs (MBPs) and mindfulness practice. This PhD thesis aimed to further understanding of the effectiveness of different doses related to MBPs and practices through a comprehensive review and experimental studies. A large-scale dose-response meta-regression including 203 randomised controlled trials (both, compared to inactive and active controls) was completed with 15 dose variables related to MBPs and practice. The outcomes were depression, anxiety, stress, and mindfulness at post-program and follow-up. The meta-regression showed significant dose-response relationships between doses related to actual program use, face-to-face contact, and program intensity and the mindfulness outcome. No robust significant dose-response relationships were found for psychological distress outcomes. Actual amount of mindfulness practice was frequently not consistently and reliably recorded in the studies included in the dose-response review. Additionally, the review did not support causal conclusions. Therefore, a randomised controlled experiment examined the relative effectiveness of longer (20-minute) and shorter (5-minute) mindfulness practices in a general population sample of novice practitioners. Although both doses were found effective at reducing psychological distress and increasing mindfulness compared to control, results showed that shorter practices had a significantly greater positive effect on mindfulness and stress than longer practices. Additionally, the effectiveness of a single-dose mindfulness practice was assessed. An online-delivered randomised experiment, with a general population sample, examined the effects of a mindfulness induction on state hope and gratitude. This induction had significant positive effects on both outcomes, and state mindfulness statistically mediated the improvements in state hope and gratitude. Overall, thesis findings have contributed to the field of mindfulness research by showing that higher and lower MBP and mindfulness practice doses are helpful, but that for novices, lower mindfulness practice doses may be more effective, especially in self-help MBPs without an experienced teacher present

Study on open science: The general state of the play in Open Science principles and practices at European life sciences institutes

Nowadays, open science is a hot topic on all levels and also is one of the priorities of the European Research Area. Components that are commonly associated with open science are open access, open data, open methodology, open source, open peer review, open science policies and citizen science. Open science may a great potential to connect and influence the practices of researchers, funding institutions and the public. In this paper, we evaluate the level of openness based on public surveys at four European life sciences institute