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Zeder, M. A. (2011). Pathways to animal domestication. In A. Damania, & P. Gepts (Eds.), Harlan II: Biodiversity in Agriculture: Domestication, Evolution, and Sustainability. Davis: University of California.
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Burn, C. C. (2011). A Vicious Cycle: A Cross-Sectional Study of Canine Tail-Chasing and Human Responses to It, Using a Free Video-Sharing Website. Plos One, 6(11), e26553.
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.
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Kwang Ng Aik, & Rodrigues Daphne. (2011). A Big-Five Personality Profile of the Adaptor and Innovator. J. Creativ. Behav., 36(4), 254–268.
Abstract: This study explored the relationship between two creative styles (adaptor and innovator) and the Big Five personality traits (extraversion, agreeableness, conscientiousness, neuroticism, and openness to experience). 164 teachers from 3 secondary and 2 primary schools in Singapore completed a self?report questionnaire, which consisted of the Kirton Adaption?Innovation Inventory and the NEO?Five Factor Inventory. It was found that adaptors were significantly more conscientious than innovators, while innovators were significantly more extraverted and open to experience than adaptors. No significant differences were found between adaptors and innovators in neuroticism and agreeableness. The study also revealed a meaningful pattern of relationships between the Big Five personality traits and the three facet scales of the KAI. Specifically, Sufficiency of Originality was negatively correlated with Openness to Experience and Extraversion; Rule Governance was positively correlated with conscientiousness but negatively correlated with openness to experience; Efficiency was positively correlated with conscientiousness. The overall findings supported the fundamental contention that different creative styles were due to different combinations of personality traits, with adaptors being more conscientious, while innovators being more extraverted and open to experience. These personality?based differences in creative styles between adaptors and innovators had resulted in much social conflict between them. One way of resolving it is to make known the nature and value of different creative styles to these two different types of creators.
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Gille, C., Hoischen-Taubner, S., & Spiller, A. (2011). Neue Reitsportmotive jenseits des klassischen Turniersports. Sportwissenschaft, 41(1), 34–43.
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.
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A. Wiggins, & K. Crowston. (2011). From Conservation to Crowdsourcing: A Typology of Citizen Science. In 2011 44th Hawaii International Conference on System Sciences (pp. 1–10). 2011 44th Hawaii International Conference on System Sciences.
Abstract: Citizen science is a form of research collaboration involving members of the public in scientific research projects to address real-world problems. Often organized as a virtual collaboration, these projects are a type of open movement, with collective goals addressed through open participation in research tasks. Existing typologies of citizen science projects focus primarily on the structure of participation, paying little attention to the organizational and macrostructural properties that are important to designing and managing effective projects and technologies. By examining a variety of project characteristics, we identified five types-Action, Conservation, Investigation, Virtual, and Education- that differ in primary project goals and the importance of physical environment to participation.
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Bücheler, T., & Sieg, J. H. (2011). Understanding Science 2.0: Crowdsourcing and Open Innovation in the Scientific Method. Proceedings of the 2nd European Future Technologies Conference and Exhibition 2011 (FET 11), 7, 327–329.
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.
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Stenglein, J. L., Waits, L. P., Ausband, D. E., Zager, P., & Mack, C. M. (2011). Estimating gray wolf pack size and family relationships using non invasive genetic sampling at rendezvous sites. J Mammal, 92.
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Core Development Team, R. (2011). R: a language and environment for statistical computing. Vienna, Austria: R foundation for statistical computing.
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Marescot, L., Pradel, R., Duchamp, C., Cubaynes, S., Mrboutin, E., & Choquet, R. (2011). Capture – recapture population growth rate as a robust tool against detection heterogeneity for population management. Ecol Appl, 21.
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Morgan, T. W., & Elliott, C. L. (2011). Comparison of remotely-triggered cameras vs. howling surveys for estimating coyote (Canis latrans) Abundance in central Kentucky. J Ky Acad Science, 72.
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