Ownership Class Research Articles

Silicon Valley's Team: The Golden State Warriors, Datafied Managerialism, and Basketball's Racialized Geography

Abstract: This essay examines how the Golden State Warriors' multimedia empire invites viewers to embrace Silicon Valley–driven transformations of space and body that appropriate value generated by Black, Brown, and working-class communities for the benefit of a wealthy, white ownership class. These transformations form part of the tech industry's imperializing adventures in bodily and societal improvement through the intertwined processes of disruption, technological solutionism, datafication, and financial speculation. First, we show how the Warriors promote analytics, wearable technology, surveillance, and white managerialism as keys to success on and off the court. We then turn to the team's 2019 move from Oakland's Oracle Arena to San Francisco's Chase Center, which offered investment and networking opportunities for Silicon Valley elites while making the team less affordable and physically accessible to its traditional Black and working-class Oakland fanbase. Ultimately, we argue that the Warriors promote Silicon Valley processes of wealth extraction by obscuring where and how value is generated, both within the labor relations that define the Warriors' sports organization and in the gentrification of the Bay Area and the commodification of Black Oakland for an increasingly non-Black fanbase.

Playbor, gamble-play, and the financialization of digital games

This article examines non-fungible token (NFT) applications and their users through a qualitative textual analysis of NFT-based video game Axie Infinity’s Discord server. It considers NFT applications’ dual purposes as entertainment media and financial instruments and posits that the interests of capital inform users’ engagement. In an environment defined by distrust and uncertainty, predominantly Filipino digital laborers’ (“Scholars”) experiences and interactions with the game’s ownership class (“Managers”) reflect pre-existing patterns of exploitation made inexpensive by differences in currency valuations, accessible by access to digital devices, available by global financial uncertainty, possible by a lack of user protection and governance, and permissible by light government regulation. To navigate an interplay of designed systems and human behavior, users share gameplay and marketplace knowledge. The blurring of gaming, gambling, and finance discussed here risks fostering an increasingly gamified approach to work and finance and facilitates exploitation of global, stratified labor.

Political Domination and Economic Dispossession of Farmers: The Case of Land Acquisition for Special Economic Zones in India

Abstract Involuntary acquisition of agricultural land for setting up of Special Economic Zones (SEZ) in India benefited the elite at the expense of small farmers who were and are the dominant voting group. Consequently, such policies were met with fierce political resistance by farmer organizations across the country. However, these movements have a mixed record against land acquisition attempts by the state and large corporations. This paper presents a simple model of the political conflict between the elite and small farmers over land acquisition to show how the elite may mobilize resources to ensure that their economic interests are protected even in democracies where they are in electoral minorities. We test the predictions of our model using a new data set compiled on SEZ projects that failed to acquire land because of farmer agitations. We show that factors like inequality in land ownership (class) and hierarchies of caste hinder the ability of small and marginal farmers to successfully organize collective action against land acquisition. Further, the division of votes along caste and ethnic lines also dilutes the potential for successful farmer agitations against land acquisition. Finally, we find that historically marginalized communities also resist land acquisition even when they face greater caste-based discrimination in the traditional village economy.

Small-Area Estimation for the USDA Forest Service, National Woodland Owner Survey: Creating a Fine-Scale Land Cover and Ownership Layer to Support County-Level Population Estimates

Small area estimation is a powerful modeling technique in which ancillary data can be utilized to “borrow” additional information, effectively increasing sample sizes in small spatial, temporal, or categorical domains. Though more commonly applied to biophysical variables within the study of forest inventory analyses, small area estimation can also be implemented in the context of understanding social values, behaviors, and trends among types of forest landowners within small domains. Here, we demonstrate a method for deriving a continuous fine-scale land cover and ownership layer for the state of Delaware, United States, and an application of that ancillary layer to facilitate small-area estimation of several variables from the USDA Forest Service’s National Woodland Owner Survey. Utilizing a proprietary parcel layer alongside the National Land Cover Database, we constructed a continuous layer with 10-meter resolution depicting land cover and land ownership classes. We found that the National Woodland Owner Survey state-level estimations of total acreage and total ownerships by ownership class were generally within one standard error of the population values calculated from the raster layer, which supported the direct calculation of several population-level summary variables at the county levels. Subsequently, we compare design-based and model-based methods of predicting commercial harvesting by family forest ownerships in Delaware in which forest ownership acreage, taken from the parcel map, was utilized to inform the model-based approach. Results show general agreement between the two modes, indicating that a small area estimation approach can be utilized successfully in this context and shows promise for other variables, especially if additional variables, e.g., United States Census Bureau data, are also incorporated.

Modeling vehicle ownership with machine learning techniques in the Greater Tamale Area, Ghana.

Vehicle ownership modeling and prediction is a crucial task in the transportation planning processes which, traditionally, uses statistical models in the modeling process. However, with the advancement in computing power of computers and Artificial Intelligence, Machine Learning (ML) algorithms are becoming an alternative or a complement to the statistical models in modeling the transportation planning processes. Although the application of ML algorithms to the transportation planning processes—like mode choice, and traffic forecasting and demand modeling—have received much attention in research and abound in literature, scanty attention is paid to its application to vehicle ownership modeling especially in the context of small to medium cities in developing countries. Therefore, this study attempts to fill this gap by modeling vehicle ownership in the Greater Tamale Area (GTA), a typically small to medium city in Ghana. Using a cross sectional survey of formal sectors workers, data was collected between June–August 2018. The study applied nine different ML classification algorithms to the dataset using 10-fold cross-validation technique/s and the Cohen-Kappa static/statistic to evaluate the predictive performance of each of the algorithms, and the Permutation Feature Importance to examine the features that contribute significantly to the prediction of vehicle ownership in GTA. The results showed that Linear Support Vector Classification (LinearSVC) classifier performed well in comparison with the other classifiers with regards to the overall predictive ability of the classifiers. In terms of class predictions, K- Nearest Neighbors (KNN) classifier performs well for no-vehicle class whiles Linear Support Vector Classification (LinearSVC) and GaussianNB classifiers performs well for motorcycle ownership. LinearSVC and Logistic Regression classifiers performed well on the car ownership class. Also, the results indicated that travel mode choice, average monthly income, average travel distance to workplace, average monthly expenditure on transport, duration of travel to workplace, occupational rank, age, household size and marital status were significant in predicting vehicle ownership for most of the classifiers. These findings could help policies makers carve out strategies that would reduce vehicle ownership but improve personal mobility.

Neoliberal Capitalism in the Indian Organized Crime Fiction of Vikram Chandra and Salman Rushdie

Close study reveals the systemically interwoven nature of the criminal and licit sectors of capitalist economies, yet capitalist society seeks to legitimize the latter sector by attempting to hegemonically externalize or Other the former. It often does so by associating the criminal sector with stigmatized minority and/or immigrant groups, who are blamed for all of society's ills. Placing blame in this way allows the capitalist ownership class to falsely pass itself off as virtuous and free of the taint of criminality or of having engaged in criminal acts. There is a systematic sociocultural denial of the fact that capitalism produces all forms of conceivable capitalist accumulation, regardless of whether they accord with received notions of morality or legality. This essay argues that Vikram Chandra's Sacred Games and Salman Rushdie's novels The Moor's Last Sigh and The Golden House challenge this hegemonic Manichean conceptualization of crime and capitalism by thematizing the close relationships between capitalism and organized criminality in India. In the face of a socioeconomic system whose spiraling material inequalities are eroding democracy and fueling the rise of fundamentalist nationalisms, these novels counter the hegemonic legitimizing narratives that present success within the world of neoliberal capitalism as a function of meritocratic entrepreneurialness. They also present a perspective on organized crimes that resists the doxa that criminal acts and capitalist successes are wholly discrete, disparate phenomena.

Ownership Patterns Drive Multi-Scale Forest Structure Patterns across a Forested Region in Southern Coastal Oregon, USA

Research Highlights: We used airborne lidar to assess the multi-scalar patterns of forest structure across a large (471,000 hectare), multi-owner landscape of the Oregon Coast Range, USA. The results of this study can be used in the development and evaluation of conservation strategies focused on forest management. Background and Objectives: Human management practices reflect policy and economic decisions and shape forest structure through direct management and modification of disturbance regimes. Previous studies have found that land ownership affects forest cover, patch dynamics, structure, and ecosystem function and services. However, prior assessments of forest structure across landscapes and ownerships have been limited by a lack of high-fidelity forest structure measurements across a large spatial extent. We addressed three research questions: (1) What distinct classes of forest structure exist across our study area? (2) How does the distribution and pattern of forest structure vary among types of owners at scales of patches, ownership types, and subregion, and is this independent of property size? and (3) What implications do the fine and sub-regional scale patterns have for landscape configuration goals under recent updates to the Northwest Forest Plan? Materials and Methods: We examined forest structure patterns by identifying six statistically distinct classes of forest structure and then examining their distribution across and within ownership types. We used these structure classes to examine their area within each ownership class, mean patch size, and intermixing at multiple scales. Results and Conclusion: We found that the six different forest structure classes in the study area can be interpreted as two assemblages: production-style forests, principally on private lands, and structurally complex forests, principally on public lands. We found that land ownership objectives resulted in distinct landscape patterns of forest structure as measured by mean structure class patch size and intermixing of different structure class patches. Finally, we found that forest structure differed between public and private lands but differed comparatively little among ownership types within those two broad categories.

Small-Area Family Forest Ownerships in the USA

Sixty percent of family forest ownerships in the United States of America (USA) own between 0.4 and 4.0 ha (1–9 ac). Yet, little is known about this segment of family forest ownerships because they are often excluded from data collection or analyses. We utilized national data for the USA collected through the National Woodland Owner Survey to examine small-area ownerships, as well as compare attributes of this ownership group to larger-area ownerships. Small-area ownerships are less active than ownerships of greater than 4.0 ha, but not inactive. Approximately 60% of small-area ownerships have undertaken at least one of the following activities in the previous 5 years: harvesting for personal use, treating invasive plants, reducing fire hazards, and reducing unwanted insects/diseases. While 74% of small-area ownerships rate wildlife habitat protection as an important ownership objective, only 11% have undertaken a wildlife habitat improvement project in the previous 5 years. Both small- and large- (> 4.0 ha) area ownership classes are most interested in the amenity aspects of woodland ownership, but small-area ownerships are significantly less interested in ownership for firewood, timber, recreation and hunting on their land than larger ownerships. Neither ownership class is very likely to have received professional advice about their forestland in the previous 5 years, but the percentage of small-area ownerships who have done so is half that of larger-area ownerships. Both area ownership classes want their forestland to remain forested in the future. Our findings suggest needs and opportunities to engage small-area forest landowners, but likely in different ways and on different topics than when dealing with larger-area ownerships.

A first estimate of the structure and density of the populations of pet cats and dogs across Great Britain.

Policy development, implementation, and effective contingency response rely on a strong evidence base to ensure success and cost-effectiveness. Where this includes preventing the establishment or spread of zoonotic or veterinary diseases infecting companion cats and dogs, descriptions of the structure and density of the populations of these pets are useful. Similarly, such descriptions may help in supporting diverse fields of study such as; evidence-based veterinary practice, veterinary epidemiology, public health and ecology. As well as maps of where pets are, estimates of how many may rarely, or never, be seen by veterinarians and might not be appropriately managed in the event of a disease outbreak are also important. Unfortunately both sources of evidence are absent from the scientific and regulatory literatures. We make this first estimate of the structure and density of pet populations by using the most recent national population estimates of cats and dogs across Great Britain and subdividing these spatially, and categorically across ownership classes. For the spatial model we used the location and size of veterinary practises across GB to predict the local density of pets, using client travel time to define catchments around practises, and combined this with residential address data to estimate the rate of ownership. For the estimates of pets which may provoke problems in managing a veterinary or zoonotic disease we reviewed the literature and defined a comprehensive suite of ownership classes for cats and dogs, collated estimates of the sub-populations for each ownership class as well as their rates of interaction and produced a coherent scaled description of the structure of the national population. The predicted density of pets varied substantially, with the lowest densities in rural areas, and the highest in the centres of large cities where each species could exceed 2500 animals.km-2. Conversely, the number of pets per household showed the opposite relationship. Both qualitative and quantitative validation support key assumptions in the model structure and suggest the model is useful at predicting the populations of cats at geographical scales important for decision-making, although it also indicates where further research may improve model performance. In the event of an animal health crisis, it appears that almost all dogs could be brought under control rapidly. For cats, a substantial and unknown number might never be bought under control and would be less likely to receive veterinary support to facilitate surveillance and disease management; we estimate this to be at least 1.5 million cats. In addition, the lack of spare capacity to care for unowned cats in welfare organisations suggests that any increase in their rate of acquisition of cats, or any decrease in the rate of re-homing might provoke problems during a period of crisis.

Social and biophysical variation in regional timber harvest regimes.

In terms of adult tree mortality, harvesting is the most prevalent disturbance in northeastern United States forests. Previous studies have demonstrated that stand structure and tree species composition are important predictors of harvest. We extend this work to investigate how social factors further influence harvest regimes. By coupling the Forest Inventory and Analysis database to U.S. Census and National Woodland Owner Survey (NWOS) data, we quantify social and biophysical variation in the frequency and intensity of harvesting throughout a 20-state region in the northeastern United States. Among social factors, ownership class is most predictive of harvest frequency and intensity. The annual probability of a harvest event within privately owned forest (3%/yr) is twice as high as within publicly owned forests (1.5%/yr). Among private owner classes, the annual harvest probability on corporate-owned forests (3.6%/yr) is 25% higher than on private woodlands (2.9%/yr). Among public owner classes, the annual probability of harvest is highest on municipally owned forests (2.4%/ yr), followed by state-owned forests (1.6%/yr), and is lowest on federal forests (1%/yr). In contrast, corporate, state, and municipal forests all have similar distributions of harvest intensity; the median percentage of basal area removed during harvest events is approximately 40% in these three owner groups. Federal forests are similar to private woodlands with median harvest intensities of 23% and 20%, respectively. Social context variables, including local home prices, population density and the distance to a road, help explain the intensity, but not the frequency, of harvesting. Private woodlands constitute the majority of forest area; however, demographic data about their owners (e.g., their age, educational attainment, length of land tenure, retired status) show little relationship to aggregate harvest behavior. Instead, significant predictors for harvesting on private woodlands include live-tree basal area, forest type, and distance from roads. Just as with natural disturbance regimes, harvest regimes are predictable in terms of their frequency, intensity, and dispersion; and like their natural counterparts, these variables are determined by several important dimensions of environmental context. But in contrast to natural disturbance regimes, the important dimensions of context for harvesting include a combination of social and biophysical variables.

Położenie klasowo-własnościowe graczy w profesjonalnych sportach drużynowych

Although social sciences do not ignore sports altogether, they do not attempt to analyse the class position of players in team sports. Yerefore, the aim of this paper is to conduct such analysis. Ye starting point is introduction of the idea of socio-economic class based on the theory of economic ownership understood as rent. As the term "economic rent" is used in neoclassical economy, I explain the di>erences between that approach and the one used in this paper. Building on theory and on empirical data on the earnings of players, I conclude that while the sports elite undoubtedly represent ownership and bourgeoisie class, the less !nancially attractive leagues or less earning players represent workers owning labour and sometimes that ownership is severely limited.

Nasionalisme dan kelas sosial: Ideologi dan praktik partai nasionalis di Indonesia

This study explores poor people or negative ownership class phenomena as result of exploitative ideology by using power including exploitation of natural resource and placement of population as cheap labor. This study explores poor people or negative ownership class phenomena as result of exploitative ideology by using power, including exploitation of natural resource and placement of human resources as cheaper labor. Although many studies about Indonesian nationalism were conducted previously, but generally the study of nationalism in relation to the poor people class in Indonesia are not the main focus of the study.This article using qualitative research method. In order to compare different phenomena in various periods, this article uses historical comparative research method. There were five levels in historical comparative research. First, is conceptualizing the object of inquiry. Second is locating the evidence. Third is evaluating quality of evidence. Fourth is organizing evidence. Fifth is syntesizing. An open interviewed was conducted with stakeholders whose directly involved in many cases within several political party bodies such as Partai Nasional Indonesia (PNI), Partai Demokrasi Indonesia (PDI). Data was analyzed by social, cultural, and political interpretation. It can be concluded tht in national politic constellation, the phenomena of poor class produce a defense term for their group, known as 'marhaen' and 'wong cilik'. Nevertheless, nationalism practice in Indonesia as represented by PNI, PDI, dan PDI Perjuangan, have not yet succedeed to freed poor people from social aleniation in political power contestation, the ownership of capital, or in relations between state and government. This nation still struggled to build competitive political and economical power in order to reate a social transformation.

Urbanization Effects on Timberland by Ownership Class: A Modified Multinomial Logit Analysis

Abstract In this study, a modified multinomial logit approach is used to examine the determinants of timberland by ownership class using county level data in Georgia from 1972 to 2000. We model timberland use by private industry ownership and nonindustrial private forestland ownership, in addition to agriculture, urban uses, and other land uses. Urbanization and other socioeconomic variables have different levels of influence on timberland ownership classes, private forest industry, and nonindustrial private forestland owners. The findings may have implications for land use modeling and projections.

California Logging Utilization: 2004

Abstract Astudy of logging activities conducted during 2004 provided utilization data and information on timber harvesting operations in California. A nested and stratified sampling scheme was used to produce a sample of felled trees with distributions of geographic area, ownership class, tree species, and tree size representative of California's recent sawlog and veneer log harvest. Results of the study indicated that about 50% of the harvested trees were less than 16.5-in.dbh, but these trees produced just 15% of the volume. About 50% of the harvested volume came from trees less than 24.5-in. dbh, and about two-thirds of the volume was from trees less than 30-in. dbh. Removals factors, quantifying impacts on growing stock, revealed that 1,051.4 ft3 of growing-stock volume was removed for every thousand cubic feet delivered to mills, with just 61.5 ft3 left in the forest as logging residue. Periodic reevaluations of logging utilization in California would make it possible to evaluate impacts of technology, market conditions, and policy changes on logging operations and utilization factors in the state.

White-Tailed Deer Management Practices on Private Lands in Arkansas

Development of management plans for white-tailed deer (Odocoileus virginianus) requires that states examine how multiple biological and regulatory factors (i.e., season timing, hunting access, bag limits) influence populations. In the southeastern United States, harvest restrictions often exceed state regulations on private lands. Thus, better information regarding harvest management on private lands is needed by wildlife agencies when developing management plans. We received responses from 1,184 white-tailed deer hunting camps registered in the Arkansas Deer Camp Program (DCP) to evaluate management practices used on private lands in Arkansas. We found that 60% of respondents used harvest restrictions in excess of state regulations. Most differences in harvest restrictions were attributed to involvement in Quality Deer Management (QDM) programs. Harvest restrictions also differed by property ownership class and deer management unit (DMU). Hunting camps were more likely to be under QDM when working with Arkansas Game and Fish Commission (AGFC) biologists. Camps under QDM were more likely to use restrictions limiting harvest of adult males. Aggregate effects of localized management (e.g., age- and sex-specific harvest) may influence population structure over broad geographic scales. Therefore, comprehensive management planning and evaluation of white-tailed deer harvest regulations must consider the multitude of harvest practices implemented by private land managers. We recommend that state wildlife management agencies conduct population studies to determine impacts of localized harvest restrictions on population dynamics occurring at broader geographic areas.

Survival Analysis for a Large-Scale Forest Health Issue: Missouri Oak Decline

Survival analysis methodologies provide novel approaches for forest mortality analysis that may aid in detecting, monitoring, and mitigating of large-scale forest health issues. This study examined survivor analysis for evaluating a regional forest health issue - Missouri oak decline. With a statewide Missouri forest inventory, log-rank tests of the effects of covariates on the survivor function and equality of the survivor function among classes were conducted for selected oak species. Additionally, hazard functions were determined for diameter classes for damaged and undamaged oaks. Results indicate that mortality appears to vary significantly among some inventory classes such as oak species, but not among other classes such as ownership class. Indicators of individual tree vigor (i.e., crown class and ratio) were more significant predictors of oak tree mortality than site/stand attributes (i.e., density and aspect). Finally, results indicate that even fast-growing oak trees are at high risk of mortality if damaged by disease. Survival analyses, such as those applied in this study, may enable testing of forest health hypotheses using large-scale inventories. In the context of Missouri's oak forest decline, study results suggest management efforts should focus on limiting the spread of disease damage, increasing the vigor of residual trees, and emphasizing small trees when developing stand prescriptions.

Forming Life Satisfaction among Different Social Groups during the Modernization of China

Quality of life has become a notable concern in the modernization of China. However, there are no adequate data to chart the formation of quality of life, in terms of life satisfaction, and in relation to the societal conditions of modernization. Moreover, it is still a question concerning how life satisfaction forms among people of different social groups, differentiated by class, education, age, and gender. To answer the questions, the present study involved a random sample survey of 732 inhabitants in Beijing. Results indicate the contributions of income, age, perceived modern economic conditions in society, and satisfaction with societal economic conditions. In contrast, education and the ownership class appear to predict lower life satisfaction. Furthermore, there are significant variations in the effects of the factors in predicting the life satisfaction of people in different social groups. Notably, perceived economic conditions appear to be less important to people of higher class positions.

Sustaining family forests in rural landscapes: Rationale, challenges, and an illustration from oregon, USA

Family forests are critical components of rural landscapes, societies and economies. In Oregon, where nonindustrial private forests comprise only 16% of the forestland base, the ecological, social and economic impact of this ownership category is disproportionately large. This is due to the landscape position these lands occupy, the diversification they contribute to forest cover and local economies, and the political and cultural connections they provide to urban populations. The significance of this ownership category is even greater in the United States as a whole, where nonindustrial private forests comprise nearly two-thirds of the commercial forestland base, dominating rural landscapes in many regions of the country. Despite the important role family forests play, their ability to contribute to the wellbeing of rural areas is challenged by several dynamic factors, including industrial consolidation in global wood markets, loss of family forestland to corporate ownership, and parcelization and fragmentation of family forestland at the urban fringe. Moreover, family forestry does not enjoy a strong social contract with the American public, which is largely ignorant of the existence of this ownership class. A foundation of broad social approval and appreciation for family forestry is a prerequisite to development of policies which can sustain family forestland ownerships and the contributions they make. This paper draws from recent research in Oregon to argue that, whereas most research on nonindustrial private forests has focused on economics and management at the individual producer level, these challenges demand greater attention to the role of family forests in the wider context of landscape, culture and rural economy.

A Regional Landscape Analysis and Prediction of Favorable Gray Wolf Habitat in the Northern Great Lakes Region

Over the past 15 years the endangered eastern timber wolf (Canis lupus lycaon) has been slowly recolonizing northern Wisconsin and, more recently, upper Michigan, largely by dispersing from Minnesota (where it is listed as threatened). We have used geographic information systems (GISs) and spatial radiocollar data on recolonizing wolves in northern Wisconsin to assess the importance of factors in defining favorable wolf habitat. We built a multiple logistic regression model applied to the northern Great Lakes states to estimate the amount and spatial distribution of favorable wolf habitat at the regional landscape scale. Our results suggest that areas with high probability of favorable habitat are more extensive than previously estimated in the northern Great Lake States. Several variables were significant in comparing new pack areas in Wisconsin to nonpack areas, including land ownership class, land cover type, road density, human population, and spatial landscape indices such as fractal dimension (land cover patch boundary complexity), land cover type contagion, landscape diversity, and landscape dominance. Road density and fractal dimension were the most important predictor variables in the logistic regression models. The results indicate that public forest land and private industrial forest land are both important in managing for a broad‐ranging animal such as the wolf. Our data portray favorable habitat that is highly fragmented along development corridors in northern Wisconsin, which may be responsible for the slow growth of the wolf population. Upper Michigan, which is just beginning to be colonized by wolves, has very large, contiguous areas of likely habitat approaching the importance of those in northeastern Minnesota. If continuing development or wolf control restrict dispersing wolves from moving from Minnesota to Wisconsin, and Wisconsin habitat becomes more marginal through further fragmentation, Michigan has the potential to maintain a significant wolf population independent of Minnesota and serve as a source population for Wisconsin. However, a simple island/corridor model of wolf habitat in Wisconsin does not seem to apply. Wolves apparently move throughout the landscape, across many unfavorable areas, but establishment success is restricted to higher quality habitat. Source‐sink dynamics may be operating here, and they suggest that reduction of the Minnesota population in the near term may affect recovery in Wisconsin and Michigan. Our analysis is an example of use of long‐term monitoring data and large‐scale cross‐boundary regional analysis that must be done to solve complex spatial questions in resource management and conservation.

Employee shareholders in two privatised utilities

This article draws on a survey of over 400 employees in two privatised companies. It examines the relationship between the depth of share ownership and social class and prior political orientations, and suggests that employee attitudes are best understood as a function of these standard sociological factors.