Society without the social: the Spanish labour movement and the rise of the social, 1840–1880

Focusing on the Spanish case, this article addresses two fundamental questions: why were many unionized workers sceptical about state intervention in labour issues throughout the nineteenth century, and why did this attitude begin to change from the 1860s onwards? Its main thesis is that workers’ attitudes derived ultimately from different historical notions of ‘society’ that shaped their perceptions and experiences of labour relations and their attitude toward the role of the state. Thus, a notion of society as an aggregation of individuals shaped unionized Spanish workers’ hostility toward state intervention since the creation of the first unions in the 1840s. From the 1860s onward, a new conception of collective relations, namely ‘the social’, began to transform some workers’ expectations of the role of the state in labour conflicts. The main factor that explains this change, it is argued, lies in the relationship between the workers’ imaginary, their actions, and their expectations about these actions.     

Bayesian Classifiers Applied to the Tennessee Eastman Process

Fault diagnosis includes the main task of classification. Bayesian networks (BNs) present several advantages in the classification task, and previous works have suggested their use as classifiers. Because a classifier is often only one part of a larger decision process, this article proposes, for industrial process diagnosis, the use of a Bayesian method called dynamic Markov blanket classifier that has as its main goal the induction of accurate Bayesian classifiers having dependable probability estimates and revealing actual relationships among the most relevant variables. In addition, a new method, named variable ordering multiple offspring sampling capable of inducing a BN to be used as a classifier, is presented. The performance of these methods is assessed on the data of a benchmark problem known as the Tennessee Eastman process. The obtained results are compared with naive Bayes and tree augmented network classifiers, and confirm that both proposed algorithms can provide good classification accuracies as well as knowledge about relevant variables.     

Caloric restriction minimizes aging effects on the femoral medial condyle

This study aimed to analyze the effects of caloric restriction on aged femoral articular cartilage of Wistar rats. Three groups of eight animals each were considered: young (YC) and old (OC) control groups fed with a normal diet and old caloric restriction group (OCR) composed of 18-month-old animals fed with a 31% less caloric diet from 6-months of age. Articular cartilage was studied through morphometry and immunohistochemistry. Body mass was 12% less in the OCR group than in the OC group. The articular cartilage from OC rats show thinner medial condyles, fewer chondrocytes, smaller chondrocytes nuclear volume and, in both condyles, a predominance of collagen type II and less collagen density compared to both YC and OCR groups (p?p?相似文献   

Percentile Related Bounds and Estimator for the Mean of the Negative Binomial Distribution

Upper and lower bounds are obtained for the mean of the negative binomial distribution. These bounds are simple functions of a percentile determined by the shape parameter. The result is then used to obtain a robust estimate of the mean when the shape parameter is known.     

Unraveling quantity discounts

We consider the situation in which a buying organization deals with a discrete quantity discount schedule offered by a selling organization. Furthermore, the buying organization can negotiate with the selling organization about the lot size and purchase price, but does not know the underlying function that was used by the selling organization to determine the quantity discount schedule. In this paper, we provide an analytical and empirical basis for one general quantity discount function (QDF) that can be used to describe the underlying function of almost all different quantity discount types. We first develop such a QDF analytically. Among other things, this QDF enables buying organizations to calculate detailed prices for a large number of quantities. We subsequently show that the QDF fits very well with 66 discount schedules found in practice. We discuss that the QDF and related indicators can be a useful tool in supplier selection and negotiation processes. It can also be used for competitive analyses, multiple sourcing decisions, and allocating savings for purchasing groups. Additionally, the QDF can be included in research models incorporating quantity discounts. We conclude the paper with an outlook on further QDF research regarding the characterization of commodity markets from a demand elasticity point of view.     

Characteristics of a central change programme within a governmental bureaucracy: a grounded theory study

Purpose The purpose of the paper is to present a theory of organisational change within the setting of a governmental bureaucracy. Design/methodology/approach Orthodox grounded theory is employed in the setting of a change programme in 12 Audit departments of the Dutch Ministries (public-sector). Findings The examined organisational change has specific characteristics. The Dutch ‘ministerial autonomy’ and the clearly recognizable role types of the key figures involved have a strong impact on the change programme and the realisation of its goals. Research limitations/implications Findings are derived on the basis of a substantive case study of the change programme of the Dutch Departmental Audit function. The research sheds new light on characteristics related to the occupational group of Chartered Accountants and characteristics of a governmental bureaucracy and it helps to understand change programmes within this bureaucracy. Practical implications The grounded theoretical model draws attention to the impact of key figures and circumstances on a change programme within the central government. Originality/value The paper builds on theories of organisational change but applies them in the typical context of the Dutch civil service where ministerial autonomy, on the one hand, and a move in the direction of more centrally organised activities (concern idea), on the other, require a difficult balancing act of all players in the change arena.     

Identifying radon-prone building typologies by marginal modelling

Radon is a naturally occurring decay product of uranium known to be the main contributor to natural background radiation exposure. It has been established that the health risk related to radon exposure is lung cancer. In fact, radon is considered to be a major leading cause of lung cancer, second only to smoking. In this paper, we identified building typologies that affect the probability of detecting indoor radon concentration above reference values, using the data collected within two monitoring campaigns recently conducted in Northern Italy. This information is fundamental both in prevention, i.e. when the construction of a new building is planned and in mitigation, i.e. when a high concentration detected inside buildings has to be reduced. A spatial regression approach for binary data was adopted for this goal where some relevant covariates on the soil were retrieved by linking external spatial databases.     

Pandemic Recovery Analysis Using the Dynamic Inoperability Input-Output Model

Economists have long conceptualized and modeled the inherent interdependent relationships among different sectors of the economy. This concept paved the way for input-output modeling, a methodology that accounts for sector interdependencies governing the magnitude and extent of ripple effects due to changes in the economic structure of a region or nation. Recent extensions to input-output modeling have enhanced the model's capabilities to account for the impact of an economic perturbation; two such examples are the inoperability input-output model^{( 1 , 2 )} and the dynamic inoperability input-output model (DIIM).^{( 3 )} These models introduced sector inoperability, or the inability to satisfy as-planned production levels, into input-output modeling. While these models provide insights for understanding the impacts of inoperability, there are several aspects of the current formulation that do not account for complexities associated with certain disasters, such as a pandemic. This article proposes further enhancements to the DIIM to account for economic productivity losses resulting primarily from workforce disruptions. A pandemic is a unique disaster because the majority of its direct impacts are workforce related. The article develops a modeling framework to account for workforce inoperability and recovery factors. The proposed workforce-explicit enhancements to the DIIM are demonstrated in a case study to simulate a pandemic scenario in the Commonwealth of Virginia.     

Childless Elders in Assisted Living: Findings from the Maryland Assisted Living Study

We compared data drawn from a random sample of 399 current assisted living residents and a subsample of 222 newly admitted residents for two groups: childless residents and residents with children. The percentage of childless residents (26%) in our study was slightly higher than U.S. population estimates of childless individuals aged 65 years and older (20%). In the overall sample, the two groups differed significantly by age, race, and women's years of education. The childless group was slightly younger, had a higher percentage of African American residents, and had more years of education than the group with children. In the subsample, we looked at demographic, functional, financial, and social characteristics and found that childless residents reported fewer diagnoses of dementia and fewer visits from a relative but more reported paying less money per month for assisted living and having private insurance than residents with children. As childlessness among older adults continues to increase, it will become increasingly important to understand how child status affects the need for and experience of long-term care.