Level Of Fairness Research Articles

Quiet Quitting in the Hospital Context: Investigating Conflicts, Organizational Support, and Professional Engagement in Greece

Background/Objectives: Quiet quitting, defined as employees fulfilling only the minimal requirements of their roles without extra effort or engagement, poses unique challenges in high-stress environments like hospitals where commitment directly impacts patient care. This study investigates the phenomenon of “quiet quitting” within the healthcare sector, with a specific focus on hospital staff in Greece. Methods: A cross-sectional design was employed, surveying 186 healthcare professionals from the General Hospital of Argos using the Questionnaire for Conflicts in Healthcare Organizations and the Quiet Quitting Scale (QQS). Results: Descriptive and inferential statistical analyses revealed that 62% of participants exhibited characteristics of quiet quitting, with “lack of motivation” scoring highest (M = 2.80, SD = 0.987) among QQS subscales. Significant correlations were observed between perceived reward fairness and motivation levels (r = −0.194, p < 0.01) and between management awareness of contributions and both motivation (r = −0.313, p < 0.01) and initiative (r = −0.192, p < 0.01). Logistic regression identified perceptions of management awareness as a key predictor of quiet quitting (p < 0.05). Conclusions: The findings emphasize the critical role of equitable reward systems and managerial recognition in reducing disengagement. Strategies to enhance employee engagement and resolve workplace conflicts are essential for fostering a resilient healthcare workforce.

Adapting FAIR Evaluation to Photon and Neutron Facilities

The FAIR principles have become essential in establishing transparent and trustworthy research practices. However the FAIR principles are guidelines indicating the features expected for data to be FAIR, and do not stipulate evaluation criteria. Consequently,there has been a proliferation of approaches to FAIR evaluation to substantiate claims for FAIR-ness, establish baselines, and measure improvement. Some approaches are focussed on FAIR-ness of individual datasets, others of repositories; some require extensive human evaluation, others use automation. However, within some scientific domains, data generation and management follow well-defined processes that result in datasets annotated with metadata and archived in repositories. Existing FAIR evaluation methods consider in less detail the contribution of the processes used in collecting and analysing data and how these enable FAIR-ness. We describe the evaluation approach adopted for FAIR self-evaluation for Photon and Neutron Research Infrastructures (PaN RI’s). We review selected examples of existing FAIR evaluation frameworks designed to enable assessment at different levels, and outline four dimensions that characterise them. As no existing framework met our specific need to focus on FAIR workflows and processes inPaN RIs, it was necessary to select, combine, and adapt existing frameworks, and we developed an approach drawing heavily on the original FAIR principles, the RDA FAIR Data Maturity Model, and FAIRsFAIR’s CoreTrustSeal+FAIRenabling framework. Post-evaluation feedback from ExPaNDS partners indicated that they found the FAIR self-evaluation a useful and valuable exercise forunderstanding current levels of FAIR-ness at their facilities and for articulating what implementations they have in progress or planned to support FAIR in future.

Chinese-Australians, media consumption, and the politics of belonging

ABSTRACT Research in news and news consumption tells us that news operates to sustain democracy culture and facilitate a sense of belonging, but only if and when producers and audiences are ‘on the same wavelength’. At the same time, research surveys suggest that Australia’s general public and the Chinese-Australian communities are at odds when it comes to their assessments of the level of fairness and balance in Australian media’s China-related news. Given that China is Australia’s biggest trading partner while simultaneously perceived as Australia’s biggest security threat, and since China is also one of the most common countries of birth for migrants in Australia, the question naturally arises as to what extent Chinese-Australians’ news consumption shapes these migrants’ sense of belonging to Australia. Situated in the context of first-generation migrants from China, and adopting a combination of a quantitative survey, in-depth interviews, and group discussions, this paper aims to address this question about migrant belonging and media consumption. The study suggests that members of this particular Chinese-Australian cohort mostly demonstrate a sense of ‘passive belonging’, and very few of them seem to possess a sense of ‘governmental belonging’.

FedFAIM: A Model Performance-Based Fair Incentive Mechanism for Federated Learning

Federated Learning (FL) has emerged as a privacy-preserving distributed machine learning paradigm. To motivate data owners to contribute towards FL, research on FL incentive mechanisms is gaining great interest. Existing monetary incentive mechanisms generally share the same FL model with all participants regardless of their contributions. Such an assumption can be unfair towards participants who contributed more and promote undesirable free-riding, especially when the final model is of great utility value to participants. In this paper, we propose a Fairness-Aware Incentive Mechanism for federated learning (FedFAIM) to address such problem. It satisfies two types of fairness notion: 1) aggregation fairness, which determines aggregation results according to data quality; 2) reward fairness, which assigns each participant a unique model with performance reflecting his contribution. Aggregation fairness is achieved through efficient gradient aggregation which examines local gradient quality and aggregates them based on data quality. Reward fairness is achieved through an efficient Shapley value-based contribution assessment method and a novel reward allocation method based on reputation and distribution of local and global gradients. We further prove reward fairness is theoretically guaranteed. Extensive experiments show that FedFAIM provides stronger incentives than similar non-monetary FL incentive mechanisms while achieving a high level of fairness.

A General Concave Fairness Framework for Influence Maximization Based on Poverty Reward

Influence maximization (IM) aims to find a group of influential nodes as initial spreaders to maximize the influence spread over a network. Yet, traditional IM algorithms have not been designed with fairness in mind, resulting in discrimination against some groups, like LGBTQ communities and racial minorities, etc. This issue has spurred research on Fair Influence Maximization (FIM). However, existing FIM studies come with some drawbacks. Firstly, most proposed notions of fairness for FIM cannot adjust the trade-off between fairness level and influence spread. Secondly, though a few specific notions of fairness allow such balancing, they are limited to a few specific concave functions, which may not be suitable for various real-world scenarios. Furthermore, none of them have studied the deep relations between the features of concave functions and the level of fairness. Thirdly, existing fairness metrics are limited to their corresponding concepts of fairness. Comparing the level of fairness across different algorithms using existing metrics can be challenging. To tackle the above problems, this paper first proposes a novel fairness notion named Poverty Reward (PR), which achieves fairness by rewarding the enrichment of groups with low utility. Based on PR, we further propose an algorithmic framework called Concave Fairness Framework (CFF) that allows any concave function that satisfies specific requirements. We also systematically clarify how fairness is improved by applying concave functions and provide an in-depth quantitative analysis of how to select appropriate concave functions for different utility distributions. Moreover, we propose the Reward of Fairness (RoF) metric that evaluates the disparity between groups. Based on RoF, an evaluation system is built to uniformly compare FIM algorithms from different fairness notions. Experiments in real-world datasets have demonstrated the validity of the CFF, as well as the proposed fairness notion.

The reputation-based reward mechanism promotes the evolution of fairness

In real life, a good reputation generally brings positive returns to individuals. For example, merchants with numerous good reviews usually gain higher profits. Considering this in the ultimatum game, we propose a reputation-based reward mechanism to investigate the evolution of fairness. Specifically, individuals' reputations evolve dynamically based on the outcomes of games. At the same time, we set a reputation threshold in the population. When individuals' reputations exceed the reputation threshold, they are considered excellent. Otherwise, they are ordinary. The excellent individuals can receive extra rewards compared to the ordinary ones. Finally, individuals' total payoffs determine their fitness within the population. Based on these settings, this paper mainly explores how reputation threshold, weight factor and reward strength affect the evolution of fairness. Through a series of simulations, the reputation-based rewards mechanism is proved to effectively promote the fairness in the population. To be specific, we find that higher reputation thresholds and smaller values of weight factor significantly enhance the promotion effect of reward on fairness. Simultaneously, there is a specific correspondence between the reputation threshold and the weight factor. When reward strength is fixed, for different reputation thresholds, the optimal value of weight factor to achieve maximum fairness levels also varies. Additionally, increasing reward strength can significantly promote fairness.

Wearing myopia glasses on GANs: Mitigating bias for pre-trained Generative Adversarial Networks via online prior perturbation

Pre-trained Generative Adversarial Networks (GANs) can provide rich information and make various downstream tasks beneficial. However, the training process of GANs potentially learns pre-existing biases in the historical data, which are not only inherited, but also amplified to some extent when using the generated data for prediction. Most available methods for bias mitigation utilize pre-processing or in-processing strategies to retrain GANs, which needs to access the raw data and also increases the time and computational cost required to train a fully expressive GAN. Therefore, we propose a novel post-processing mechanism to achieve fairness outputs of GANs through online prior perturbation. Unlike traditional output-based offline processing, our method mitigates biases from model inputs by designing a prior perturbation network, called “prior perturber”, to form a combined network with the pre-trained GAN. Specifically, we introduce a bias prediction network, called “bias predictor”, for online adversarial training with the prior perturber, which can decouple the generated representations from the bias features and improve the fairness of the downstream prediction results. In addition, the training process is independent of specific target labels, and the generated fair representations have good transferability. Experimental evaluations on five real datasets validate the effectiveness of the proposed method and the best utility is obtained on the same fairness level as compared to other output-based baseline methods. The result on fairness-utility trade-offs significantly improves the fairness of original samples by 56.40%, while the utility is reduced by only 2.60%.

Seeking a pathway towards a more sustainable human-water relationship by coupled model – From a perspective of socio-hydrology

It is essential to systematically consider social, economic, and natural endowments in managing and allocating water resources. However, few studies have comprehensively quantitatively evaluated the allocation of regional water resources from a socio-hydrology perspective and provided recommendations. To explore this research gap, we have constructed a tightly coupled framework that integrates system dynamics models and optimization algorithms to carry out an innovative redistribution of water resources in Shaanxi Province. The system dynamics model simulation results showed that the error was almost always within 10% over the research period, indicating robust simulation capability and laying a solid foundation for subsequent model coupling. The coupled model achieves convergence in approximately 30 generations by formulating the optimization problem with four individual objectives. Optimizing four objectives concurrently results in convergence around the 150th generation. The optimized Pareto solution sets visually demonstrate the trade-offs between different objectives. In the optimized water allocation schedule, the water consumption in Yulin exhibits a change of 1.22 ×108m3, reflecting the most significant optimization effects on agricultural and domestic water allocation. The results indicated that the comprehensive Gini coefficient typically ranged between 0.2 and 0.3. Over the period from the year 2010–2021, the Gini coefficient exhibited a declining trend, signifying a positive trajectory in water resource allocation throughout the research period and a high level of fairness. The annual total green WF of grain in Weinan was the highest at 14.26 ×108m3, followed by Xianyang at 9.52 ×108m3, and the lowest in Tongchuan at 0.54 ×108m3. The annual average amount of blue WF of grain is the highest in Hanzhong, at 11.33 ×108m3, followed by Weinan at 9.60 ×108m3, and the lowest in Tongchuan at 0.14 ×108m3. The coupled framework proposed in this study exhibits significant innovation, scalability, and practical efficiency. It can inspire future research and decision-making and holds the potential for application in other regions.

Evaluating and mitigating unfairness in multimodal remote mental health assessments.

Research on automated mental health assessment tools has been growing in recent years, often aiming to address the subjectivity and bias that existed in the current clinical practice of the psychiatric evaluation process. Despite the substantial health and economic ramifications, the potential unfairness of those automated tools was understudied and required more attention. In this work, we systematically evaluated the fairness level in a multimodal remote mental health dataset and an assessment system, where we compared the fairness level in race, gender, education level, and age. Demographic parity ratio (DPR) and equalized odds ratio (EOR) of classifiers using different modalities were compared, along with the F1 scores in different demographic groups. Post-training classifier threshold optimization was employed to mitigate the unfairness. No statistically significant unfairness was found in the composition of the dataset. Varying degrees of unfairness were identified among modalities, with no single modality consistently demonstrating better fairness across all demographic variables. Post-training mitigation effectively improved both DPR and EOR metrics at the expense of a decrease in F1 scores. Addressing and mitigating unfairness in these automated tools are essential steps in fostering trust among clinicians, gaining deeper insights into their use cases, and facilitating their appropriate utilization.

Analysis of green supply chains under fairness concern and differential power structure

PurposeThis study aims to examine how fairness concerns and power structure in dyadic green supply chains impact retail price, supply chain profits and greening level decisions.Design/methodology/approachThis study develops game-theoretic models considering fairness concerns and asymmetric power structures under an iso-elastic demand setting. The research paper employs the Stackelberg game approach, taking into consideration the fairness concern of the channel leader.FindingsThe findings indicate that under fairness, there is an increase in both wholesale and retail prices, as well as greening expenditures. Notably, when comparing the two models (manufacturer Stackelberg and retailer Stackelberg), double marginalization is more pronounced in the retailer Stackelberg setup than in the manufacturer Stackelberg setup. In a traditional supply chain with iso-elastic demand, the follower typically extracts higher profit compared to the leader; however, our results show that, under fairness conditions, the leader achieves higher profit than the follower. Additionally, our study suggests that supply chain coordination is unattainable in a fairness setup. This paper provides insights for managers on the optimal supply chain structure and the level of fairness to maximize profit.Originality/valueThis paper investigates the impact of a leader's fairness on the optimal decisions within a green supply chain, an area that has received limited attention previously. Additionally, the study investigates how fairness concerns manifest in distinct power dynamics, specifically, in the contexts of manufacturer Stackelberg and retailer Stackelberg.

A hybrid congestion control scheme for Named Data Networking via SDN

As the mainstream congestion control in Named Data Networking (NDN), the hybrid control mechanism integrates the advantages of consumer and router control mechanisms. However, The concurrent control by both consumers and routers may results in degraded data transmission performance. Therefore, we propose a novel hybrid congestion control scheme for NDN, called ndnSieve. By using the characteristics of Software Defined Network (SDN) that can achieve global network information acquisition as well as global resource scheduling, ndnSieve manages to invoke consumer and router control mechanisms based on different congestion states in the network respectively and achieves the synergy between two kinds of mechanisms. The results of experiment demonstrate that, compared with existing works, ndnSieve has the lowest average round-trip time, relatively high level of throughput and fairness, effectively improving the Quality of Service (QoS) of the network.

Character strength traits, states, and emotional well-being: A daily diary study.

Does whole trait theory work for character strengths? This study examines the daily within- and between-person variability of the manifestations of positively valued lower-order personality characteristics, namely character strengths, their convergence with trait character strengths, and their relationships to daily measures of affect. Manifestations of personality traits vary both between- and within people. So far, research has focused on between-person differences in character strengths, while within-person differences have been neglected. German-speaking participants (N = 199, 84.3% women; mean age = 26.0 years) participated in a two-week daily diary study. They completed a baseline measure of character strength traits and daily measures of character strength states and positive and negative affect. Results suggested that character strength traits converged well with aggregated states. Further, we observed high within-person variability in most character strengths. The trait-state convergence and the amount of within-person variability were predicted by whether the character strengths were rather phasic (i.e., more dependent on situational characteristics) or rather tonic (i.e., less dependent on situational characteristics). Higher within-person variability in character strengths was related to trait levels of perspective, honesty, social intelligence, and fairness. Regarding relationships between character strengths and affect, within-person associations were widely parallel to previously reported between-person associations and largely independent of trait levels of character strengths. These findings inform research on whole trait theory and character-strengths-based interventions.

인사공정성이 조직구성원의 조직몰입과 이직의도에 미치는영향에 관한 연구

With the emphasis on fairness issues, the importance of fairness in personnel management for public servants is increasing. We examined the level of fairness in personnel management, from recruitment for public service to promotion, staffing, workload, and evaluation, and explored how fairness in personnel management affects organizational commitment and turnover intentions. Analyzing the results, firstly, the level of fairness in recruitment was the highest, and workload was the lowest. Secondly, perceived fairness in recruitment, promotion, workload, and evaluation positively influenced organizational commitment. Thirdly, perceived fairness in recruitment and workload was found to decrease turnover intentions. Based on these findings, implications were provided to enhance organizational commitment among members and reduce turnover intentions.

Critical global citizenship of high school students in Indonesia and Japan

The COVID-19 pandemic has prompted a review of global citizenship and education to tackle global injustices. This study investigates the impact of school education and the national curriculums on students’ attitudes towards global citizenship and examines the features of critical global citizenship among Indonesian and Japanese students. According to a questionnaire survey, students in both countries had high levels of altruism, empathy and non-violent aspirations of the collective and low levels of fairness of rewards and non-violent aspirations of the personal. Indonesian students had a higher sense of personal and national responsibility; however, they did not recognize the structural violence of poverty. Japanese students recognized poverty as structural violence; however, they were uninformed about global human rights and lacked a sense of personal responsibility in global issues. Online international education and curricula should be improved to enable students to understand global issues and adopt a sense of responsibility towards them.

Study on the Spatial Arrangement of Urban Parkland under the Perspective of Equity—Taking Harbin Main City as an Example

The connotation and measurement standard of “fairness” in parkland planning is closely related to the level of urban development and residents’ needs, with solid realism and dynamics, and it is still a complex problem to control accurately in planning. Therefore, to conduct a more comprehensive study, taking the main urban area of Harbin as an example, this paper combined urban development background and practical problems at different stages and, based on POI and AOI data, divided the connotation of “equity” in park green space planning into two levels: “spatial equity” and “social equity”. Based on these two levels of fairness, the research framework of urban park green space layout is constructed. Kernel density estimation and GIS-based network analysis were used to study the spatial layout and accessibility of urban parkland. The ability to match supply and demand for parkland in the study area as a whole was analyzed using a gridded two-step moving search method, as well as the degree of equity in the enjoyment of urban parkland by vulnerable groups including the elderly and children using the Lorenz curve with the Gini coefficient, the share index, and the entropy of the locality. The results show that: (1) the parkland is concentrated in the seven main urban areas of the two–two junctions, and the cohesion distribution trend is outside the distribution. (2) Motorized vehicles have the best 15-min accessibility for travel, but it still does not cover all the parkland. (3) Supply and demand within the region are unsatisfactory, with the area with the strongest supply and demand capacity for parkland accounting for only 3.19% of the total area. (4) The Gini coefficient of parkland enjoyed by the residents of Harbin city center is 0.407, with a massive gap in the overall distribution. In addition, the share index of vulnerable groups of the elderly and children all have share indices below 1, and the results of the analysis of locational entropy indicate that the level of street park green space resources enjoyed by these two groups belongs to the low socio-spatial equity pattern, which is not balanced. This study investigates the spatial layout of urban parkland from two equity levels, which can provide new ideas for the equity study of urban green space planning.

Evaluation of employees' well-being in relation to job satisfaction and the level of organisational fairness

This study focuses on evaluating employees ' job satisfaction and the degree of fairness within organizations. The main aim is to examine the relationship between these crucial elementsof the work environment and how they impact employee performance and company success. The analysis of the obtained data is a crucial element of this research, as it is necessary to assess variations in employees' perceptions and to identify potential discrepancies between satisfaction levels and fairness perceptions. This study also explores how employees' well-being, job satisfaction and organizational fairness relate to organizational performance. The results obtained from this research could provide organizations with the necessary insights to improve their employees' work environment and generate effective strategies to enhance employee job satisfaction.

Tourism ecosystem management: The role of trust and procedural justice

Ecosystem theory is becoming more prevalent in tourism as a powerful concept to explain value creation at tourism destinations, especially in developing countries. However, for well-established destinations collective action, which is a prerequisite for successful ecosystem operation, is difficult to ignite. This phenomenon can especially be observed at community-based destinations in structurally well-developed countries like Switzerland that consist of a large number of independent actors whose offerings are complementary. Together, these independent actors (henceforth called complementors) form the tourism product which is greater than the sum of its parts provided that the complementors collaborate. This study investigates if the active integration of ecosystem complementors by the destination management organization (DMO) fosters complementors’ trust towards the DMO. This is important as trust is the functional prerequisite for effective collaboration. Further, we analyze whether this effect is amplified by high levels of procedural fairness. The results show that the systematic integration of complementors positively influences complementors’ trust towards the DMO. Also, if integration processes are executed in the context of high procedural justice trust levels are further enhanced. Thus, systematic complementor integration can be a crucial and efficient step towards a functioning ecosystem at community-based destinations. The results inform DMO practitioners as well as scholars about the dynamics of successful ecosystem operation at tourism destination management.

The Use of Scoring Rubrics in University

This study examines the application of scoring rubrics as an evaluative instrument in higher education environments. Specifically, it explores the advantages, validity, and reliability associated with their use. Scoring rubrics provide a systematic and unbiased methodology for assessing various academic assignments, assisting educators in delivering constructive feedback and promoting student development. The study delves into the benefits associated with the utilization of rubrics, which encompass improved levels of transparency, uniformity, and fairness in the grading process. Furthermore, it investigates the validity and reliability of rubrics, ensuring the precision and consistency of evaluation outcomes. Moreover, this study explores the utilization of holistic and analytic rubrics within Malaysian universities, providing an analysis of their individual applications and advantages in the evaluation procedure. In the pursuit of improving assessment methods, institutions are increasingly recognizing the significance of including scoring rubrics as a valuable instrument to facilitate high-quality education and foster student achievement.

Može li veštačka inteligencija da doprinese pravednoj energetskoj tranziciji?

The level of fairness of the energy transition mainly depends on the balance of the energy trilemma in the observed country. Artificial intelligence (AI) and machine learning have the potential to improve the overall performance of the energy system. The advantage of these technologies is that they can be applied in the energy production and consumption sectors, which can maximize energy efficiency and thus reduce the level of primary energy consumption. Likewise, AI can be used to increase the level of flexibility of the power system, enabling greater inclusion of renewable sources. All this affects the reduction of emissions, which represents one of the most important norms in reaching the goals of decarbonization of the economy. In addition to the above, AI could be used to improve the reliability of energy systems and reduce the overall costs of their exploitation. There are also significant limitations in the application of artificial intelligence. First of all, there is currently a rather big problem with the adequate workforce needed for further development, which creates a certain "bottleneck" in the application in the energy sector. Also, AI's electricity consumption is not negligible, which potentially represents an additional load on production capacities. The goal of the research is to make an overview of the current applications of AI in the energy sector and to give a critical review of the impact on energy justice using those specific examples. In the research, communication with the publicly available tool based on artificial intelligence, ChatGPT, will be used in order to reach a certain set of information about the topic of interest. In this way, AI will be directly involved in the research in such a way that a set of specific questions will be asked in ChatGPT, and the answers obtained will be integral part of the final paper.

Personal Income Tax Compliance Behaviour in Kathmandu Valley

This article aimed to explore whether factors such as tax awareness and knowledge, the perceived fairness of the tax system, its complexity, tax rates, and income levels influence the compliance behavior of registered personal income taxpayers in the Kathmandu Valley. A theoretical framework was employed to test hypotheses based on responses gathered from a survey of these taxpayers. Data collection was carried out using a purposive judgmental sampling method, and questionnaires were distributed as part of a descriptive and analytical research design. The collected data were analyzed using least squares regression and SPSS 26.0. The results indicated that personal income tax compliance, the dependent variable, was significantly and positively influenced by five key factors: tax awareness and knowledge, fairness of the tax system, complexity of the tax system, tax rates, and income level. The findings also revealed that taxpayers often lack sufficient technical knowledge and perceive the tax system as overly complex. These factors—along with tax awareness, fairness, complexity, tax rates, and income level—are thought to contribute to taxpayer non-compliance. To enhance tax compliance, the study recommends that the government should focus on improving public services, increasing awareness about the benefits of paying taxes, and educating the public on compliance measures. Additionally, conducting regular audits can act as a deterrent to non-compliance. Future research could investigate the impact of social and referral groups on compliance behavior, as social norms and ethical values may offer further incentives for tax compliance.