Abstract: Rural communities are changing. Depopulation and unemployment is accompanied by the advance of new perspectives on nature, where protection trumps resource extraction. These developments are perceived as threatening by rural working-class people with close ties to traditional land use ? a situation they often meet with cultural resistance. Cultural resistance is not necessarily launched against institutionalized power, nor does it necessarily imply a desire for fundamental social change. It should rather be seen as a struggle for autonomy. However, autonomy does not entail influence outside the cultural realm. Struggles to uphold traditional rural lifestyles ? for example by denouncing the current nature conservation regime ? could be understood in much the same conceptual framework as Willis employed in ?Learning to labour?. Based on an ethnographic study of the conflicts over wolf protection, we demonstrate that ?the Hammertown mechanism? is of a more general nature than often implied in the discussion of Willis? work.

Abstract: Tail-chasing is widely celebrated as normal canine behaviour in cultural references. However, all previous scientific studies of tail-chasing or 'spinning' have comprised small clinical populations of dogs with neurological, compulsive or other pathological conditions; most were ultimately euthanased. Thus, there is great disparity between scientific and public information on tail-chasing. I gathered data on the first large (n = 400), non-clinical tail-chasing population, made possible through a vast, free, online video repository, YouTube[TM]. The demographics of this online population are described and discussed. Approximately one third of tail-chasing dogs showed clinical signs, including habitual (daily or 'all the time') or perseverative (difficult to distract) performance of the behaviour. These signs were observed across diverse breeds. Clinical signs appeared virtually unrecognised by the video owners and commenting viewers; laughter was recorded in 55% of videos, encouragement in 43%, and the commonest viewer descriptors were that the behaviour was 'funny' (46%) or 'cute' (42%). Habitual tail-chasers had 6.5+/-2.3 times the odds of being described as 'Stupid' than other dogs, and perseverative dogs were 6.8+/-2.1 times more frequently described as 'Funny' than distractible ones were. Compared with breed- and age-matched control videos, tail-chasing videos were significantly more often indoors and with a computer/television screen switched on. These findings highlight that tail-chasing is sometimes pathological, but can remain untreated, or even be encouraged, because of an assumption that it is 'normal' dog behaviour. The enormous viewing figures that YouTube[TM] attracts (mean+/-s.e. = 863+/-197 viewings per tail-chasing video) suggest that this perception will be further reinforced, without effective intervention.

Abstract: Während die traditionellen Pferdesportdisziplinen Dressur- und Springreiten Mitglieder verlieren, haben sich weitere Pferdesportarten in Deutschland etabliert und erfreuen sich wachsender Beliebtheit. Die vorliegende Arbeit beschäftigt sich mit den Hintergründen dieser Entwicklung. In einer empirischen Untersuchung wurden 1814 Reiter zu ihren Reitmotiven befragt. Mit Hilfe von Hauptkomponenten- und Clusteranalyse wurde eine Typologie gebildet, die ein differenziertes Bild der Motive verschiedener Reitergruppen ermöglicht. Während die leistungsorientierten Reiter eher in klassischen Reitsportdisziplinen vertreten sind, dominieren in moderneren Reitsportdisziplinen vor allem Genussmotive. Insgesamt entwickelt sich der Trend im Reitsport deutlich vom Drill in der Reitbahn hin zu mehr Entspannung, Erholung und Selbstverwirklichung. Der Wunsch, in der Freizeit Leistung zu bringen, sich mit anderen zu messen und Erfolg zu haben, ist nur noch für einen kleineren Teil der Pferdesportler bedeutsam. Aus der Verteilung der Motive ergeben sich neue Herausforderungen für den organisierten Reitsport, um den Spagat zwischen den Anforderungen der leistungsorientierten Sportreiter und den Erholungssuchenden zu meistern.

Abstract: The innovation process is currently undergoing significant change in many industries. The World Wide Web has created a virtual world of collective intelligence and helped large groups of people connect and collaborate in the innovation process [1]. Von Hippel [2], for instance, states that a large number of users of a given technology will come up with innovative ideas. This process, originating in business, is now also being observed in science. Discussions around “Citizen Science” [3] and “Science 2.0” [4] suggest the same effects are relevant for fundamental research practices. “Crowdsourcing” [5] and “Open Innovation” [6] as well as other names for those paradigms, like Peer Production, Wikinomics, Swarm Intelligence etc., have become buzzwords in recent years. However, serious academic research efforts have also been started in many disciplines. In essence, these buzzwords all describe a form of collective intelligence that is enabled by new technologies, particularly internet connectivity. The focus of most current research on this topic is in the for-profit domain, i.e. organizations willing (and able) to pay large sums to source innovation externally, for instance through innovation contests. Our research is testing the applicability of Crowdsourcing and some techniques from Open Innovation to the scientific method and basic science in a non-profit environment (e.g., a traditional research university). If the tools are found to be useful, this may significantly change how some research tasks are conducted: While large, apriori unknown crowds of “irrational agents” (i.e. humans) are used to support scientists (and teams thereof) in several research tasks through the internet, the usefulness and robustness of these interactions as well as scientifically important factors like quality and validity of research results are tested in a systematic manner. The research is highly interdisciplinary and is done in collaboration with scientists from sociology, psychology, management science, economics, computer science, and artificial intelligence. After a pre-study, extensive data collection has been conducted and the data is currently being analyzed. The paper presents ideas and hypotheses and opens the discussion for further input.