Joint enterprise, murder and substantial injustice: the first successful appeal post-Jogee (case comment)

This paper analyses the Court of Appeal decision in R v Crilly [2018] EWCA Crim 168. This was the first out-of-time appeal after R v Jogee in which the applicant succeeded in demonstrating ‘substantial injustice’ and having his murder conviction vacated. Although the judgment demonstrates that the Court remains faithful to its approach in R v Johnson and the high threshold test of ‘substantial injustice', Crilly suggests that if it can be shown that an applicant's case was in essence about foresight, the odd reference to intention might not prove fatal to demonstrating that had the jury been given Jogee-compliant directions on accessorial liability, this would have made a difference to their verdict

Oblique intent, foresight and authorisation

In R v Jogee, the Supreme Court of the United Kingdom (UKSC) abolished a contentious doctrine of criminal law which allowed accomplices to a crime A to be convicted of another’s crime B on the basis that they foresaw commission of the latter in the course of the former. The Court held that nothing short of an intention to assist or encourage crime B would suffice to fix the accomplice with criminal liability. At common law intention has traditionally been understood to entail acts and consequences that were either achieved with purpose (direct intent) or foreseen as virtually certain to follow one’s chosen course of conduct (oblique intent). This paper argues that Jogee constitutes a first step away from a conception that measures ‘guilty minds’ in degrees of foresight: by associating the accessory’s intent to assist or encourage the perpetrator’s crime with ‘authorisation’, Jogee seems to support the view that intention in the legal sense depends ultimately on whether the accused had endorsed the consequences of his and the perpetrator’s actions

Models of Cognition: Neurological possibility does not indicate neurological plausibility

Many activities in Cognitive Science involve complex computer models and simulations of both theoretical and real entities. Artificial Intelligence and the study of artificial neural nets in particular, are seen as major contributors in the quest for understanding the human mind. Computational models serve as objects of experimentation, and results from these virtual experiments are tacitly included in the framework of empirical science. Cognitive functions, like learning to speak, or discovering syntactical structures in language, have been modeled and these models are the basis for many claims about human cognitive capacities. Artificial neural nets (ANNs) have had some successes in the field of Artificial Intelligence, but the results from experiments with simple ANNs may have little value in explaining cognitive functions. The problem seems to be in relating cognitive concepts that belong in the `top-down' approach to models grounded in the `bottom-up' connectionist methodology. Merging the two fundamentally different paradigms within a single model can obfuscate what is really modeled. When the tools (simple artificial neural networks) to solve the problems (explaining aspects of higher cognitive functions) are mismatched, models with little value in terms of explaining functions of the human mind are produced. The ability to learn functions from data-points makes ANNs very attractive analytical tools. These tools can be developed into valuable models, if the data is adequate and a meaningful interpretation of the data is possible. The problem is, that with appropriate data and labels that fit the desired level of description, almost any function can be modeled. It is my argument that small networks offer a universal framework for modeling any conceivable cognitive theory, so that neurological possibility can be demonstrated easily with relatively simple models. However, a model demonstrating the possibility of implementation of a cognitive function using a distributed methodology, does not necessarily add support to any claims or assumptions that the cognitive function in question, is neurologically plausible

The U.S. refugee admissions program in Austin : the story of one Congolese family

textIn 2013, the U.S. Department of State allocated nearly $45 million to the Texas Office of Refugee Resettlement to help aid with the resettlement of 6,922 refugees. Approximately 10 percent of all refugees who were brought to the U.S. that year were resettled in Texas. Austin received 716 of those refugees. For 30 years, the U.S.'s Refugee Admissions Programs has been providing aid and money to help refugees fleeing religious and political persecution resettle in the U.S. The program is a system of public-private partnership in which the U.S. Department of State hands out funds to local non-profit organizations to oversee the initial six months of the resettlement. The current program is largely underfunded and is based on a self-sufficiency model that requires refugees find a job within four months of arrival setting many refugees on a path towards poverty as they are often come from conflict zones with minimal English skills, knowledge of how the U.S. works or programs to help them use whatever skills or education they have to find better paying jobs. This paper critiques elements of the U.S. Refugee Admissions Program through the case study of one family that has recently arrived in Austin, Texas, from the Democratic Republic of the Congo.Journalis

Magnetoresistor monitors relay performance

Magnetoresistor monitors the action of relays without disturbing circuit parameters or degrading relay performance. The magnetoresistor measures the relay magnetic flux produced under transient conditions to establish the characteristic signature of the relay

Identifying collapsed buildings

THE WORK TO RECOVER AND REBUILD FOLLOWING an earthquake requires reliable information on the condition of structures in the affected areas. In developed areas, efforts to gather this information can be time-consuming and prone to errors, often resulting in incomplete or inaccurate information. A new, software-based methodology to recognize collapsed buildings utilizes classification of satellite images combined with height variation information. The methodology was demonstrated in a full-scale, real-life scenario by a team led by Prof. Valerio Baiocchi of the University of Rome. According to Baiocchi, the team’s work was intended to demonstrate the methodology on actual data available for the entire city of L’Aquila in the Abruzzo region of central Italy, in an actual and complete simulation of quick damage assessment in a real emergency. The team utilized satellite imagery of the city of L’Aquila, which experienced a magnitude 6.3 earthquake on April 6, 2009. The work demonstrated a robust classification of collapsed structures that was completed quickly and with good confidence