Karl Marx （1973） scientifically predicted the appearance of some extraordinary tendencies of social development in the second half of the 20th century was given a common name of post-industrial or informational soci...Karl Marx （1973） scientifically predicted the appearance of some extraordinary tendencies of social development in the second half of the 20th century was given a common name of post-industrial or informational society and interpreted as post-bourgeois, post-capitalist, post-business society, and late capitalism. Autonomist Marxism and Perm philosophy school had separately come to a conclusion that all the phenomena noticed by the post-industrial theory could be adequately explained if we consider the historically new form of material labour appearing now. Marx （1973）, who predicted this new form, named it automated, scientific, or universal labour. With the appearance of the universal labour, the wealth of the society depends on the universal human powers that help to involve the extensive powers of nature into the production process. Universal labour can not be averaged or measured by the labour time as the abstract labour; it implies high complexity and creativity. Involving increasingly powerful forces of nature and human society, it appears to be the labour of another essence and by its essence, it does not create value.展开更多
In order to receive a licence to produce, poultry farmers have to take into account societal demands, among others： animal welfare, healthy working conditions for the workers and landscape quality. A way to reach a c...In order to receive a licence to produce, poultry farmers have to take into account societal demands, among others： animal welfare, healthy working conditions for the workers and landscape quality. A way to reach a combination of these goals is to create a design for the poultry house and outdoor run. We propose a methodology based on five steps, which enables us to create a design that takes into consideration societal demands and that can be tested on its effects. These five steps are： 1. Giving a theoretical background on the societal demands （hen ethology, farm management and landscape quality） and based on this; 2. Giving a set of design criteria. 3. Describing the cttrrent state of the farm, in order to know its current qualities, 4. Making a design of the farm using the sets of criteria as guiding principle. 5. Reflecting on the design, to show whether the different criteria can be combined and where compromises are needed. A case study on an organic farm in the centre of the Netherlands showed that hen welfare, farm management and landscape quality can be improved together, although some measures do not add to all design criteria. Especially the effect on landscape quality and farm management is variable： the latter is also depending on the personal motivation of the farmer.展开更多
It is well understood that for conventional survey designs the set of unordered distinct units in a sample is a minimally sufficient statistic. This means that for inferential statistic of the sample, the value of the...It is well understood that for conventional survey designs the set of unordered distinct units in a sample is a minimally sufficient statistic. This means that for inferential statistic of the sample, the value of the sampled units rather than the sample design is important. Sampling rare populations presents distinct challenges. Examples of rare populations are in biology with rare and endangered animals where there are only a few remaining individuals, or in social science, with the low incidence of people from an unusually high （or low） income group. Sampling rare populations tends to result in the case that many of the sample units do not contain information on the characteristic of interest （e.g., the rare animal, or people from the unusual income group）. For finite rare populations the set of unordered distinct rare-units in a sample is a minimally sufficient statistic. In an example case study of a rare buttercup, the properties of the minimal sufficient estimator are explored. We compare the efficiency of the estimator for the population total based on the minimally sufficient statistic, with the standard estimator for a range of sample sizes. The variance of the minimally sufficient estimator was always smaller than the variance of the sufficient estimator. For rare populations where non-rare units can be distinguished from rare units because they have the same fixed value, the minimal sufficient statistic is the rare units, if any, in the sample.展开更多
文摘Karl Marx （1973） scientifically predicted the appearance of some extraordinary tendencies of social development in the second half of the 20th century was given a common name of post-industrial or informational society and interpreted as post-bourgeois, post-capitalist, post-business society, and late capitalism. Autonomist Marxism and Perm philosophy school had separately come to a conclusion that all the phenomena noticed by the post-industrial theory could be adequately explained if we consider the historically new form of material labour appearing now. Marx （1973）, who predicted this new form, named it automated, scientific, or universal labour. With the appearance of the universal labour, the wealth of the society depends on the universal human powers that help to involve the extensive powers of nature into the production process. Universal labour can not be averaged or measured by the labour time as the abstract labour; it implies high complexity and creativity. Involving increasingly powerful forces of nature and human society, it appears to be the labour of another essence and by its essence, it does not create value.
文摘In order to receive a licence to produce, poultry farmers have to take into account societal demands, among others： animal welfare, healthy working conditions for the workers and landscape quality. A way to reach a combination of these goals is to create a design for the poultry house and outdoor run. We propose a methodology based on five steps, which enables us to create a design that takes into consideration societal demands and that can be tested on its effects. These five steps are： 1. Giving a theoretical background on the societal demands （hen ethology, farm management and landscape quality） and based on this; 2. Giving a set of design criteria. 3. Describing the cttrrent state of the farm, in order to know its current qualities, 4. Making a design of the farm using the sets of criteria as guiding principle. 5. Reflecting on the design, to show whether the different criteria can be combined and where compromises are needed. A case study on an organic farm in the centre of the Netherlands showed that hen welfare, farm management and landscape quality can be improved together, although some measures do not add to all design criteria. Especially the effect on landscape quality and farm management is variable： the latter is also depending on the personal motivation of the farmer.
文摘It is well understood that for conventional survey designs the set of unordered distinct units in a sample is a minimally sufficient statistic. This means that for inferential statistic of the sample, the value of the sampled units rather than the sample design is important. Sampling rare populations presents distinct challenges. Examples of rare populations are in biology with rare and endangered animals where there are only a few remaining individuals, or in social science, with the low incidence of people from an unusually high （or low） income group. Sampling rare populations tends to result in the case that many of the sample units do not contain information on the characteristic of interest （e.g., the rare animal, or people from the unusual income group）. For finite rare populations the set of unordered distinct rare-units in a sample is a minimally sufficient statistic. In an example case study of a rare buttercup, the properties of the minimal sufficient estimator are explored. We compare the efficiency of the estimator for the population total based on the minimally sufficient statistic, with the standard estimator for a range of sample sizes. The variance of the minimally sufficient estimator was always smaller than the variance of the sufficient estimator. For rare populations where non-rare units can be distinguished from rare units because they have the same fixed value, the minimal sufficient statistic is the rare units, if any, in the sample.
