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The problem of workers at risk should be reframed to reflect the impact of social stratification, power relations and divergent interests in occupational health practices…
The problem of workers at risk should be reframed to reflect the impact of social stratification, power relations and divergent interests in occupational health practices. The past two decades have seen rapid developments in technology for detecting genetic traits and abnormalities in individuals that may indicate damage from chemical exposure. Occupational physicians, industrial managers and biomedical scientists increasingly favour this technology. However these methods have only selective appeal and are quite controversial. Their accuracy in identifying high‐risk workers is disputed as well as their value and consequences. Social factors that shape the way workers at risk have been defined are discussed. These social processes help to explain the way issues of risk are framed and industrial practices are conducted. They also explain patterns of support and opposition to genetic technology.
Presents the scientific methodology from the enlarged cybernetical perspective that recognizes the anisotropy of time, the probabilistic character of natural laws, and the…
Presents the scientific methodology from the enlarged cybernetical perspective that recognizes the anisotropy of time, the probabilistic character of natural laws, and the entry that the incomplete determinism in Nature opens to the occurrence of innovation, growth, organization, teleology communication, control, contest and freedom. The new tier to the methodological edifice that cybernetics provides stands on the earlier tiers, which go back to the Ionians (c. 500 BC). However, the new insights reveal flaws in the earlier tiers, and their removal strengthens the entire edifice. The new concepts of teleological activity and contest allow the clear demarcation of the military sciences as those whose subject matter is teleological activity involving contest. The paramount question “what ought to be done”, outside the empirical realm, is embraced by the scientific methodology. It also embraces the cognitive sciences that ask how the human mind is able to discover, and how the sequence of discoveries might converge to a true description of reality.
In the knowledge economy, greater togetherness is the prerequisite for innovating and having more: selflessness extends scope while selfishness increases limitations. But…
In the knowledge economy, greater togetherness is the prerequisite for innovating and having more: selflessness extends scope while selfishness increases limitations. But human beings are not automatically attracted to innovation: between the two lies culture and cultural values vary widely, with the egoistic accent or the altruistic intonation setting the scene. In the representations of open innovation we submit to the reader’s attention, selfishness and selflessness are active in the cultural space.
Popularized in the early 2000s, open innovation is a systematic process by which ideas pass among organizations and travel along different exploitation vectors. With the arrival of multiple digital transformative technologies and the rapid evolution of the discipline of innovation, there was a need for a new approach to change, incorporating technological, societal and policy dimensions. Open Innovation 2.0 (OI2) – the result of advances in digital technologies and the cognitive sciences – marks a shift from incremental gains to disruptions that effect a great step forward in economic and social development. OI2 seeks the unexpected and provides support for the rapid scale-up of successes.
‘Nothing is more powerful than an idea whose time has come’ – this thought, attributed to Victor Hugo, tells us how a great deal is at stake with open innovation. Amidon and other scholars have argued that the twenty-first century is not about ‘having more’ but about ‘being more’. The promise of digital technologies and artificial intelligence is that they enable us to extend and amplify human intellect and experience. In the so-called experience economy, users buy ‘experiences’ rather than ‘services’. OI2 is a paradigm about ‘being more’ and seeking innovations that bring us all collectively on a trajectory towards sustainable intelligent living.
Purpose – The purpose of this chapter is to show that because the evolutionary roots of many kinds of phenotypic social phenomena can be partly traced to genotypic factors, it would be useful for social sciences to adopt a socio-biological research formula, which combines the impacts of genotypic and environmental explanatory factors.
Design/methodology/approach – The exclusion of evolutionary factors from social sciences and some previous studies using evolutionary factors is first reviewed, after which a socio-biological research formula (y=(a+b)+x) is introduced. It is hypothesized that national IQ as an important genotypic factor explains a significant part of the global variation in all kinds of phenotypic social phenomena. The hypothesis is tested and the use of the socio-biological research formula is illustrated by studies of democratization (ID-10) and human development (HDI-11).
Findings – The results of correlation analysis support the hypothesis on the evolutionary variable’s (national IQ) universal explanatory power. National IQ explains 33 percent of the variation in ID-10 and 68 percent of the variation in HDI-11. Environmental variables increase significantly the explained part of variation in a dependent variable in the case of ID-10 but less in the case of HDI-11.
Practical implications – Because it is evident that national IQ as an evolutionary variable explains a significant part of the variation in all kinds of phenotypic social phenomena, it would be sensible for social sciences to adopt the suggested socio-biological research formula based on the idea that intelligence constitutes an important common explanatory factor.
Originality/value – The suggested socio-biological research formula provides for the social sciences a common theoretical starting point to study many kinds of social problems.
The term ‘integrative levels’ was introduced by Joseph Needham in 1937. He recognized a series of eight levels. Others have since proposed various different and often longer series. Now, by the rigorus application of two ad hoc rules or criteria for the discrimination of ‘major integrative levels’, the number of such major levels (on present knowledge) is found to be nine. Short terms are available for designating the members of the different levels. Having performed the formal classification of objects, according to integrative level, several new quantitative generalizations become apparent. For instance, there is now clear evidence of a general long‐term acceleration throughout most of the period of biological and social evolution.