843 resultados para Legal anthropology
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From 2008-2012, a dramatic upsurge in incidents of maritime piracy in the Western Indian Ocean led to renewed global attention to this region: including the deployment of multi national naval patrols, attempts to prosecute suspected pirates, and the development of financial interdiction systems to track and stop the flow of piracy ransoms. Largely seen as the maritime ripple effect of anarchy on land, piracy has been slotted into narratives of state failure and problems of governance and criminality in this region.
This view fails to account for a number of factors that were crucial in making possible the unprecedented rise of Somali piracy and its contemporary transformation. Instead of an emphasis on failed states and crises of governance, my dissertation approaches maritime piracy within a historical and regional configuration of actors and relationships that precede this round of piracy and will outlive it. The story I tell in this work begins before the contemporary upsurge of piracy and closes with a foretaste of the itineraries beyond piracy that are being crafted along the East African coast.
Beginning in the world of port cities in the long nineteenth century, my dissertation locates piracy and the relationship between trade, plunder, and state formation within worlds of exchange, including European incursions into this oceanic space. Scholars of long distance trade have emphasized the sociality engendered through commerce and the centrality of idioms of trust and kinship in structuring mercantile relationships across oceanic divides. To complement this scholarship, my work brings into view the idiom of protection: as a claim to surety, a form of tax, and a moral claim to authority in trans-regional commerce.
To build this theory of protection, my work combines archival sources with a sustained ethnographic engagement in coastal East Africa, including the pirate ports of Northern Somalia, and focuses on the interaction between land-based pastoral economies and maritime trade. This connection between land and sea calls attention to two distinct visions of the ocean: one built around trade and mobility and the other built on the ocean as a space of extraction and sovereignty. Moving between historical encounters over trade and piracy and the development of a national maritime economy during the height of the Somali state, I link the contemporary upsurge of maritime piracy to the confluence of these two conceptualizations of the ocean and the ideas of capture, exchange, and redistribution embedded within them.
The second section of my dissertation reframes piracy as an economy of protection and a form of labor implicated within other legal and illegal economies in the Indian Ocean. Based on extensive field research, including interviews with self-identified pirates, I emphasize the forms of labor, value, and risk that characterize piracy as an economy of protection. The final section of my dissertation focuses on the diverse international, regional, and local responses to maritime piracy. This section locates the response to piracy within a post-Cold War and post-9/11 global order and longer attempts to regulate and assuage the risks of maritime trade. Through an ethnographic focus on maritime insurance markets, navies, and private security contractors, I analyze the centrality of protection as a calculation of risk and profit in the contemporary economy of counter-piracy.
Through this focus on longer histories of trade, empire, and regulation my dissertation reframes maritime piracy as an economy of protection straddling boundaries of land and sea, legality and illegality, law and economy, and history and anthropology.
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When farmers need to harvest a large amount of crops in a short period of time, migrant, seasonal, and H-2A Visa workers can often be the best solution to complete the job quickly and affordably. However, there are specific Federal and state legal duties and responsibilities for farmers who employ these types of workers and substantial criminal and civil penalties for failing to adhere to the law.
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SCOPUS: ar.j
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p.205-212
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This paper presents a formalism for representing temporal knowledge in legal discourse that allows an explicit expression of time and event occurrences. The fundamental time structure is characterized as a well‐ordered discrete set of primitive times, i.e. non‐decomposable intervals with positive duration or points with zero duration), from which decomposable intervals can be constructed. The formalism supports a full representation of both absolute and relative temporal knowledge, and a formal mechanism for checking the temporal consistency of a given set of legal statements is provided. The general consistency checking algorithm which addresses both absolute and relative temporal knowledge turns out to be a linear programming problem, while in the special case where only relative temporal relations are involved, it becomes a simple question of searching for cycles in the graphical representation of the corresponding legal text.
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In judicial decision making, the doctrine of chances takes explicitly into account the odds. There is more to forensic statistics, as well as various probabilistic approaches which taken together form the object of an enduring controversy in the scholarship of legal evidence. In this paper, we reconsider the circumstances of the Jama murder and inquiry (dealt with in Part I of this paper: "The Jama Model. On Legal Narratives and Interpretation Patterns"), to illustrate yet another kind of probability or improbability. What is improbable about the Jama story, is actually a given, which contributes in terms of dramatic underlining. In literary theory, concepts of narratives being probable or improbable date back from the eighteenth century, when both prescientific and scientific probability was infiltrating several domains, including law. An understanding of such a backdrop throughout the history of ideas is, I claim, necessary for AI researchers who may be tempted to apply statistical methods to legal evidence. The debate for or against probability (and especially bayesian probability) in accounts of evidence has been flouishing among legal scholars. Nowadays both the the Bayesians (e.g. Peter Tillers) and Bayesioskeptics (e.g. Ron Allen) among those legal scholars whoare involved in the controversy are willing to give AI researchers a chance to prove itself and strive towards models of plausibility that would go beyond probability as narrowly meant. This debate within law, in turn, has illustrious precedents: take Voltaire, he was critical of the application or probability even to litigation in civil cases; take Boole, he was a starry-eyed believer in probability applications to judicial decision making (Rosoni 1995). Not unlike Boole, the founding father of computing, nowadays computer scientists approaching the field may happen to do so without full awareness of the pitfalls. Hence, the usefulness of the conceptual landscape I sketch here.
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Lennart Åqvist (1992) proposed a logical theory of legal evidence, based on the Bolding-Ekelöf of degrees of evidential strength. This paper reformulates Åqvist's model in terms of the probabilistic version of the kappa calculus. Proving its acceptability in the legal context is beyond the present scope, but the epistemological debate about Bayesian Law isclearly relevant. While the present model is a possible link to that lineof inquiry, we offer some considerations about the broader picture of thepotential of AI & Law in the evidentiary context. Whereas probabilisticreasoning is well-researched in AI, calculations about the threshold ofpersuasion in litigation, whatever their value, are just the tip of theiceberg. The bulk of the modeling desiderata is arguably elsewhere, if one isto ideally make the most of AI's distinctive contribution as envisaged forlegal evidence research.
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Review of: Handbook of Psychology in Legal Contexts. Ray Bull and David Carson (eds.) Wiley-Blackwell. 1999.
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This Acknowledgement refers to the special issue "Formal Approaches to Legal Evidence" of the Artificial Intelligence and Law, September 2001, Vol. 9, Issue 2-3, which was guest edited by Ephraim Nissan.
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This special issue "Formal Approaches to Legal Evidence" of the Artificial Intelligence and Law, September 2001, Vol. 9, Issue 2-3, which was guest edited by Ephraim Nissan.
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For the purposes of starting to tackle, within artificial intelligence (AI), the narrative aspects of legal narratives in a criminal evidence perspective, traditional AI models of narrative understanding can arguably supplement extant models of legal narratives from the scholarly literature of law, jury studies, or the semiotics of law. Not only: the literary (or cinematic) models prominent in a given culture impinge, with their poetic conventions, on the way members of the culture make sense of the world. This shows glaringly in the sample narrative from the Continent-the Jama murder, the inquiry, and the public outcry-we analyse in this paper. Apparently in the same racist crime category as the case of Stephen Lawrence's murder (in Greenwich on 22 April 1993) with the ensuing still current controversy in the UK, the Jama case (some 20 years ago) stood apart because of a very unusual element: the eyewitnesses identifying the suspects were a group of football referees and linesmen eating together at a restaurant, and seeing the sleeping man as he was set ablaze in a public park nearby. Professional background as witnesses-cum-factfinders in a mass sport, and public perceptions of their required characteristics, couldn't but feature prominently in the public perception of the case, even more so as the suspects were released by the magistrate conducting the inquiry. There are sides to this case that involve different expected effects in an inquisitorial criminal procedure system from the Continent, where an investigating magistrate leads the inquiry and prepares the prosecution case, as opposed to trial by jury under the Anglo-American adversarial system. In the JAMA prototype, we tried to approach the given case from the coign of vantage of narrative models from AI.