Suchergebnisse

The trigger for this seminar had been the publication earlier in the year of the book 'Embattled Media: Democracy, Governance and Reform in Sri Lanka'. One of the main aims of the book was to analyse issues of media practice and regulation - both abuses and the potential reform of those abuses – in the specific circumstances of Sri Lanka. The seminar had a broader aim to explore and compare thinking and practice affecting media and journalism in countries with other legal and constitutional foundations and cultural inheritances. We aim to go beyond the parameters of the book and hear from speakers with wide experience of the dilemmas facing policy makers in high income, middle income and low income countries. The three distinguished speakers in the seminar's second session will reflect on best practice in Commonwealth countries regarding the promotion of media development and the safeguarding of media freedoms.

Introduction -- Chapter 1: Family matters : understanding the importance of social context, in particular the importance of family -- Chapter 2: Despair vs Hope : defining prison as either a place of self-destruction or of self-discover and rehabilitation -- Chapter 3: The right path : assisting prisoners on their journey of rehabilitation with spiritual care and support -- Chapter 4: Restorative over retribution : the role of restorative justice in repairing relationships destroyed by crime and stemming the rate of recidivism -- Chapter 5: Second chances : taking the difficult step to re-enter society and regain credibility in the community -- Chapter 6: Taking action : in search of effective responses to crime : lesson learnt and recommendations -- Conclusions : the next steps.

Adverse drug events (ADEs) are a major concern and point of emphasis for the medical profession, government, and society in general. When methods extract ADEs from observational data, there is a necessity to evaluate these methods. More precisely, it is important to know what is already known in the literature. Consequently, we employ a novel relation extraction technique based on a recently developed probabilistic logic learning algorithm that exploits human advice. We demonstrate on a standard adverse drug events data base that the proposed approach can successfully extract existing adverse drug events from limited amount of training data and compares favorably with state-of-the-art probabilistic logic learning methods.