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Within a gated community, management of common property presents great challenges. Therefore, the diagnostic social ecological system (SES) framework proposed by Elinor Ostrom providing a holistic understanding of complex collective action problems in terms of management of commons is used to investigate key institutional-social-ecological factors influencing collective action in the context of gated communities.

In this paper, Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) was used to systematically screen and review the relevant literature from 2000 to 2022, where 28 papers were selected for further analysis.

The study systematically identifies and categorises a series of variables related to self-organizing management in the gated community, and consequently a SES-based gated community management framework is developed. Based on the conceptual framework, the paper discusses logical interrelationships of institutional-social-ecological factors and their impacts on collective action performance of gated communities.

Apart from requiring empirical validation, the conceptual SES-based gated community management framework is certainly subject to continuous improvement in terms of refinement and addition of other potential determinants of gated community collective action.

Not only the review paper provides updates on the latest gated-community collective action research, it also contributes theoretically by conceptualizing the SES framework and its institutional–social–ecological design principles in gated community management. Studying these factors should also be of practical significance because the findings ultimately offer policy insights and management strategies that help policy-makers, property developers and local communities to govern such neighbourhood common resources efficiently and sustainably.

By juxtaposing Paulston’s [Paulston, R. G. (1999). Mapping comparative education after postmodernity. Comparative Education Review, 43(4), 438–463; Paulston, 1977] demonstration of relationships between theories of social and educational change/‘reform’ and Delamont’s Four great gates: dilemmas, directions and distractions in educational research (2007), this metanarrative explores how comparative and international education research in Australia and China has evolved in terms of self-determination, planned change and strokes of luck. The tendency to adopt a deterministic/fatalistic perspective in comparative education research in this part of the world rises from the general perception that, unlike its North American and European counterparts, the field is too narrowly defined by local institutions [Denman, B. D., & Higuchi, S. (2012). At a crossroads? Comparative and international education research in Asia and the Pacific. Asian Education and Development Studies, 2(1), 4–21] and often resorts to ‘hits’ and ‘misses’. By utilizing Paulston’s work and comparing it with Delamont’s, this analysis serves as a stop-gap measure to not only help justify its means and recognize its potential but also to counter the persistent dissatisfaction that scholars try to prove and promote comparative education as a field of study in the region. In addition, the terms of reference of self-determination, planned change and strokes of luck are broadly interpreted metaphorically, using the iChing or Book of Changes (Van Over, 1971), in order to help rationally order and rhetorically clarify trends in educational scholarship, policy and practice. In the Asia and Pacific region generally, comparative and international education research can be viewed as different ways of thinking and knowing, regardless of the research methods employed. The trends, challenges, opportunities and risks associated with the field are identified as location-specific, time-sensitive and culturally unique.

Leaders, consultants and researchers are increasingly focused on enhancing the adoption of social networking tools to increase knowledge sharing practices and the success rate of organizations. This study was conducted to explore the adoption of social networking applications in public and private sector universities. This paper aims to discover how social networking applications can foster knowledge sharing practices among employees of universities.

The study is based on an interpretivist, qualitative research design using grounded theory. Fifty-two semi-structured and non-directive interviews were undertaken with employees of public and private sector universities. Participants were selected using purposive sampling, and thematic analysis was performed using the NVivo 11-Plus.

The study highlights how social media networking applications can be used effectively and efficiently to foster knowledge sharing practices in the workplace. Five emerging themes are identified as follows. Social media networking tools can enhance new knowledge, increase employee skills, promote a knowledge sharing culture, foster effective communication and increase employee involvement in research activities.

Social networking applications have received attention because executives and researchers are increasingly focused on finding new ways to use social networking tools in business. The effective and efficient use of social networking tools helps organizations to foster knowledge amongst employees, and can address various critical issues such as knowledge hoarding, lowers levels of skills and knowledge, poor communication and employee involvement, the lack of desire to share knowledge, and resistance toward technology.

A brief systematic literature review on social media and knowledge sharing highlights that only 11 per cent of studies found that examined the link between social media and knowledge sharing practices across the world. The study therefore represents an effort to shed new light on the adoption of social networking tools in the context of knowledge sharing among universities employees. Social media applications have become popular across the world, and the speed of their uptake is evolving rapidly. However, their contribution toward organizational change is not yet known.

Peer reviewer evaluations of academic papers are known to be variable in content and overall judgements but are important academic publishing safeguards. This article introduces a sentiment analysis program, PeerJudge, to detect praise and criticism in peer evaluations. It is designed to support editorial management decisions and reviewers in the scholarly publishing process and for grant funding decision workflows. The initial version of PeerJudge is tailored for reviews from F1000Research's open peer review publishing platform.

PeerJudge uses a lexical sentiment analysis approach with a human-coded initial sentiment lexicon and machine learning adjustments and additions. It was built with an F1000Research development corpus and evaluated on a different F1000Research test corpus using reviewer ratings.

PeerJudge can predict F1000Research judgements from negative evaluations in reviewers' comments more accurately than baseline approaches, although not from positive reviewer comments, which seem to be largely unrelated to reviewer decisions. Within the F1000Research mode of post-publication peer review, the absence of any detected negative comments is a reliable indicator that an article will be ‘approved’, but the presence of moderately negative comments could lead to either an approved or approved with reservations decision.

PeerJudge is the first transparent AI approach to peer review sentiment detection. It may be used to identify anomalous reviews with text potentially not matching judgements for individual checks or systematic bias assessments.

The purpose of this case study is to report and reflect on a recently completed five-year programme of research on dementia care and practice in England. This EVIDEM programme of research was specifically designed to influence services for people with dementia and their carers; several additional lessons emerged along the way that might shape broader research on ageing that includes older people and those who work with them.

This case study of the EVIDEM programme presents and discusses four lessons learned by the core research team – covering the implications of newly basing research inside the NHS, multi-disciplinary working across academic disciplines, communicating with diverse practitioners, and the impact of patient and public involvement on the research process. The paper reflects on communication between the NHS and academic communities, and the creation of new research capacity in dementia.

Collaborative working between academic disciplines is possible, given willing researchers and commitment to participating in frequent opportunities for dialogue and learning. In research in dementia these foundations are probably essential, given the growing scale of the problem and the small size of the research community, if we are to have a beneficial impact on people's lives. Lay expertise is a necessary ingredient of research programmes, not just for its co-design power, but for its ability to redesign projects when major problems arise.

This case study reports the subjective views of the research collaborators. While this raises the potential for bias, it offers an “insider” perspective of the research process and engagement in research leadership.

There are few reflections on research processes and management and this case study may be useful to academic researchers, to those working in the NHS with responsibility for research in different forms, and to older people's organisations who wish to hear of the value of older people's engagement in research advisory activity.

It has been argued in the literature that structural changes in the financial markets, such as integration, have the potential to cause herding behavior or correlated behavioral patterns in traders. The purpose of this study is to investigate whether there is any financial herding behavior in the Latin American Integrated Market (MILA), a transnational stock market composed of Chile, Peru, Colombia and Mexico stock exchanges and whether there is any ARCH or GARCH effect in the herding behavior models.

This study uses the modified return dispersion approach on daily index return data. The sample period is from January 03, 2002 to May 07, 2019. The data are obtained from the MILA database. To count time-varying volatilities in herding models, the authors run ARCH family regression with GARCH (1,1) settings. Hwang and Salmon (2004) model is used as a robustness test.

The authors found strong herding behavior under the general market conditions and moderate and partial herding behavior under some specified markets circumstances, such as bull and bear markets and high-low volatility states. Moreover, the pre-MILA period exhibits more herding behavior than the post-MILA period. The empirical results show that most of the ARCH and GARCH effects are statistically significant, implying that the past information of stock returns and market volatility significantly affect the volatility of following periods, which can also explain the formation of herding tendency among investors. Finally, the results of the robustness tests (Hwang and Salmon, 2004) confirm herding in all periods, except full sample period for Mexico and post-MILA period for Mexico and Colombia.

This study investigates the herding behavior in the MILA market in terms of market return, volatility and timing. A limitation of the paper is that the authors have not included other factors on the formation of herding behavior, such as macroeconomic factors, effects of regional or international markets and policy influences. The authors will explore the issue in the extension of the paper.

As MILA is the first virtual integration of stock exchanges without merging, the study provides useful findings and draws good inferences of herding behavior in the MILA market in terms of market return, volatility and timing which are useful for academics, investors and policymakers in their investment and decision makings.

The paper provides useful findings and draws good inferences of herding behavior in the MILA market in terms of market return, volatility and timing which are not only useful in practical implications, but also in social implications.

This study contributes to the herding literature by examining four different hypotheses in respect of the unique case of transnational stock exchange without fusions or corporate mergers, where each market maintains its independence and regulatory autonomy. The authors also contribute to the literature by including both ARCH and GARCH effects in the herding behavioral models along the Hwang and Salmon (2004) approach.

Decision support systems developed using machine learning classifiers have become a valuable tool in predicting various diseases. However, the performance of these systems is adversely affected by the missing values in medical datasets. Imputation methods are used to predict these missing values. In this paper, a new imputation method called hybrid imputation optimized by the classifier (HIOC) is proposed to predict missing values efficiently.

The proposed HIOC is developed by using a classifier to combine multivariate imputation by chained equations (MICE), K nearest neighbor (KNN), mean and mode imputation methods in an optimum way. Performance of HIOC has been compared to MICE, KNN, and mean and mode methods. Four classifiers support vector machine (SVM), naive Bayes (NB), random forest (RF) and decision tree (DT) have been used to evaluate the performance of imputation methods.

The results show that HIOC performed efficiently even with a high rate of missing values. It had reduced root mean square error (RMSE) up to 17.32% in the heart disease dataset and 34.73% in the breast cancer dataset. Correct prediction of missing values improved the accuracy of the classifiers in predicting diseases. It increased classification accuracy up to 18.61% in the heart disease dataset and 6.20% in the breast cancer dataset.

The proposed HIOC is a new hybrid imputation method that can efficiently predict missing values in any medical dataset.

Introduces the special issue on personnel economics and outlines the themes discussed in the papers contained within.

Autumn Behringer has completed her Ph.D. at Purdue University and started a position as Assistant Professor of Sociology at Weber State University in the fall of 2004. Her research centers largely on the study of gender, intimate relationships, and social inequality. Her dissertation is a symbolic interactionist analysis of marital communications. She has a chapter, “The Meaning of Husband and Wife: Spouses’ Perceptions of Marital Labels,” forthcoming in Couples, Kids, and Family Life (Oxford University Press).