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Rural education research has historically been cast in a deficit lens, with rural places characterized by their problems or shortcomings, as if the way of understanding rural itself is to compare it to nonrural locales. These intransigent and narrow perceptions of rurality hinders recognition of the assets and possibilities of rural places. The purpose of this paper is to apply community-empowering, transgressive knowledge to analyses of rural communities to advance rural education research and practice.

In this conceptual paper, the authors propose an asset-based, conceptual framework to ground rural research and education practices: rural cultural wealth.

The authors describe and explore the concept of rural cultural wealth within the context of education. Furthermore, the authors discuss the dynamics of rurality and propose four constructs that comprise the rural cultural wealth framework, rural resourcefulness, rural ingenuity, rural familism and rural community unity, and consider implications for future research and practice.

The goal of this paper is to advance a rural cultural wealth framework aimed to interrupt social reproduction of educational inequities that impact rural students.

Building on relational inequality theory, this paper incorporates social capital as a device to trace the flow of resources through relationships originating within and beyond organizations. I draw on a survey of over 1,700 lawyers to evaluate key dynamics of social capital that shape earnings: bridging and bonding, reciprocity exchanges and sponsorship, and boundary maintenance. The findings show social capital lends a lift to law graduates through bridges to professional careers and sponsorship following job entry. Racial minorities, however, suffer a shortfall of personal networks to facilitate job searches, and once having secured jobs, minorities experience social closure practices by clients and colleagues that disadvantage them in their professional work. A sizeable earnings gap remains between racial minority and white lawyers after controlling for human and social capitals, social closure practices, and organizational context. This earnings gap is particularly large among racial minorities with more years of experience and those working in large law firms. The findings demonstrate the importance of identifying the interrelations that connect social network and organizational context to impact social inequality.

This volume explores an expansive array of organizational imaginaries, or understandings of organizational possibilities, with a focus on how collectivist-democratic organizations offer alternatives to conventional for-profit managerial enterprises. These include worker and consumer cooperatives and other enterprises that, to varying degrees, (1) emphasize social values over profit; (2) are owned not by shareholders but by workers, consumers, or other stakeholders; (3) employ democratic forms of managing their operations; and (4) have social ties to the organization based on moral and emotional commitments. The contributors to this volume examine how these enterprises generate solidarity among members, network with other organizations and communities, contend with market pressures, and enhance their larger organizational ecosystems. In this introductory paper, the authors put forward an inclusive organizational typology whose continuums account for four key sources of variation – values, ownership, management, and social relations – and argue that enterprises fall between these two poles of the collectivist-democratic organization and the for-profit managerial enterprise. Drawing from this volume’s empirical studies, the authors situate these market actors within fields of competition and contestation shaped not just by state action and legal frameworks, but also by the presence or absence of social movements, labor unions, and meta-organizations. This typology challenges conventional conceptualizations of for-profit managerial enterprises as ideals or norms, reconnects past models of organizing among marginalized communities with contemporary and future possibilities, and offers activists and entrepreneurs a sense of the wide range of possibilities for building enterprises that differ from dominant models.

This paper explores how neoliberalism restrains the ability of governments to respond to crises through budgetary action. It examines the immediate budgetary responses to the COVID-19 pandemic by the Australian government and explores how the conditions created by prior neoliberal policies have limited these responses.

A review and examination of the prior literature on public budgeting and new public management are provided. The idea of a “neoliberal straitjacket” is used to frame the current budgetary and economic situation in Australia.

The paper examines the chronology of Australia's budgetary responses to the economic and health crisis created by COVID-19. These responses have taken the form of tax breaks and a temporary payment scheme for individuals made unemployed by the pandemic.

The insights gained from this paper may help with future policy developments and promote future research on similar crises.

The analysis of Australia's policies in dealing with the pandemic may offer insights for other countries struggling to cope with the fiscal consequences of COVID-19.

The purpose of this paper is to assess how employees from historically marginalized groups (men and women of color and white women) perceive Fortune’s “100 Best Companies to Work For”® (BCWF) in terms of two outcomes that are related to diversity and inclusion: fairness and camaraderie. The authors focus on fairness as a way to measure perceptions of general treatment with respect to demographic characteristics associated with bias and discrimination, and on camaraderie as a way to measure perceptions of the inclusiveness of coworker relationships.

Hierarchical linear regression models are used to analyze survey responses from 620,802 employees in 1,054 companies that applied for the BCWF list between 2006 and 2011 in the USA. The authors compare the perceptions of employees in firms that are selected for the list to those of their demographic counterparts in firms not selected for the list. The authors also compare the perceptions of employees from historically marginalized groups to those of white men within firms that make the list and examine how these differences compare to the same differences within firms that do not make the list.

The findings reveal that the perceptions of men and women of color and white women in companies that make the “best” list are more positive than their demographic counterparts in companies that do not make the list. The authors also find, however, that the perceptions of employees from historically marginalized groups are more negative than those of white men in the “best” workplaces, and these patterns are similar to those in firms that do not make the list. For perceptions of fairness, the differences between employees from historically marginalized groups and white men are smaller in companies that make the list.

The findings are based on average effect sizes across a large number of companies and employees, and the data do not provide insight into the actual organizational processes that are driving employee perceptions. In addition, the employee survey data are self-reported, and may be subject to recall and self-serving biases. Finally, the authors use measures of fairness and camaraderie that have not been rigorously tested in past research.

Managers seeking to improve experiences of fairness and camaraderie should pay particular attention to how race/ethnicity and gender influence these experiences, and how they do so intersectionally. Attending to these differences is particularly important to the extent that experiences of fairness and camaraderie are related to organizational trust, the key metric on which companies are selected for the “best” workplaces list, and a quality of organizational relationships that previous research has found to be positively related to key individual and firm-level outcomes.

The paper provides the first assessment of demographic variation in the outcomes of employees in companies selected for the BCWF. Since selection to this list is based on the presence of trust, the authors’ findings also provide potential insight into how informal organizational processes that are associated with trust, such as leadership behaviors, peer relationships, and workplace norms, are viewed and experienced by men and women of color and white women. Finally, the authors analyze outcomes relating to camaraderie, a construct that has received little attention in the literature.

This study aims to propose novel edge device-tailored federated learning algorithms of local classifiers (stochastic gradient descent, support vector machines), namely, FL-lSGD and FL-lSVM. These algorithms are designed to address the challenge of large-scale ImageNet classification.

The authors’ FL-lSGD and FL-lSVM trains in a parallel and incremental manner to build an ensemble local classifier on Raspberry Pis without requiring data exchange. The algorithms load small data blocks of the local training subset stored on the Raspberry Pi sequentially to train the local classifiers. The data block is split into k partitions using the k-means algorithm, and models are trained in parallel on each data partition to enable local data classification.

Empirical test results on the ImageNet data set show that the authors’ FL-lSGD and FL-lSVM algorithms with 4 Raspberry Pis (Quad core Cortex-A72, ARM v8, 64-bit SoC @ 1.5GHz, 4GB RAM) are faster than the state-of-the-art LIBLINEAR algorithm run on a PC (Intel(R) Core i7-4790 CPU, 3.6 GHz, 4 cores, 32GB RAM).

Efficiently addressing the challenge of large-scale ImageNet classification, the authors’ novel federated learning algorithms of local classifiers have been tailored to work on the Raspberry Pi. These algorithms can handle 1,281,167 images and 1,000 classes effectively.

For privacy protection, federated learning based on data separation allows machine learning models to be trained on remote devices or in isolated data devices. However, due to the limited resources such as bandwidth and power of local devices, communication in federated learning can be much slower than in local computing. This study aims to improve communication efficiency by reducing the number of communication rounds and the size of information transmitted in each round.

This paper allows each user node to perform multiple local trainings, then upload the local model parameters to a central server. The central server updates the global model parameters by weighted averaging the parameter information. Based on this aggregation, user nodes first cluster the parameter information to be uploaded and then replace each value with the mean value of its cluster. Considering the asymmetry of the federated learning framework, adaptively select the optimal number of clusters required to compress the model information.

While maintaining the loss convergence rate similar to that of federated averaging, the test accuracy did not decrease significantly.

By compressing uplink traffic, the work can improve communication efficiency on dynamic networks with limited resources.