"The good days are amazing", an evaluation of the Writer's in Prison Network

The Writers in Prison Network (WIPN) was established and appointed by the Arts Council in April 1998 to administer the Writers in Residence in Prison Scheme. The Scheme places writers and creative artists into prisons across the UK to deliver creative writing, drama, video, music, oral storytelling, journalism, creative reading and publishing programmes. The Scheme employs writers who are experienced or established in particular literary fields; many have been creative writing tutors, or have worked in publishing, the theatre, television, radio or journalism. In administering the Scheme, WIPN supports up to 20 Writers in Residence at any one time (with an average of 15-16 residencies per year and a maximum of 22 residencies per year undertaken during the lifetime of WIPN). In 2010 the Hallam Centre for Community Justice at Sheffield Hallam University was commissioned to undertake an evaluation of the Writers in Prison Network. The evaluation was primarily qualitative in approach which aimed to inform and support the future development of WIPN

Intensive alternatives to custody process evaluation of pilots in five areas

A qualitative process evaluation of five Intensive Alternative to Custody (IAC) pioneer areas was undertaken to assess implementation of IAC, identify approaches to implementation and capture the lessons learnt. The findings indicated that many of the persistent offenders (those with at least 29 prior convictions) targeted by pilots were positive about the IAC order. Although intensive, it provided order and stability, allowing them to move away from a criminal lifestyle. Sentencers welcomed the order as a viable alternative to custody. Probation staff and partners were equally positive about its efficacy. Only one in four IAC orders were revoked because requirements were breached, which suggests that the pilots had managed to engage many of the offenders

"I Ain't No Tea Lady": Identifying and addressing barriers to non-traditional employment, training and education from a female perspective, SOVA

The aim of this research was to examine perceptions and experiences of accessing non-traditional Education Training and Employment (ETE) from the vantage point of disadvantaged women using innovative sampling and research techniques. The research design and strategy sought to access the participant’s views and valuable experience. Many of the women whose opinions the research was trying to elicit had never considered non-traditional ETE, in their own words it simply was 'not on their radar'. We decided to adopt a 'workshop' approach. A workshop format was designed which used fun and thought provoking exercises to promote discussion. These interactive and dynamic workshops proved successful in generating some excellent data. In total 80 women from a range of areas of disadvantage participated in the research

Offenders and E-Learning - a literature review on behalf of Becta

This literature review has been prepared by the Hallam Centre of Community Justice at Sheffield Hallam University, on behalf of Becta. The literature review provides a summary of existing research and knowledge relating to e-learning in the offending learning sector with a view to developing a range of e-maturity indicators across the sector. The review also highlights linkages with current Government policy in relation to offender learning and skills

Tackling health inequalities through developing evidence-based policy and practice with childbearing women in prison: a consultation

A collaborative partnership between the Hallam Centre for Community Justice and the Mother and Infant Research Unit (MIRU) at the University of York was successful in securing funding to conduct this consultation project. This collaboration brought together the knowledge and expertise of researchers working in maternal and infant health and those with knowledge of the prison sector. This consultation scopes and maps the health needs and health care of childbearing women in prison, using the Yorkshire and Humberside region as a case study

Process evaluation of five integrated offender management pioneer areas

A qualitative process evaluation of five Integrated Offender Management (IOM) pioneer areas was undertaken to assess implementation of IOM, identify approaches to implementation and capture the lessons learnt. The findings indicated that IOM enabled structural changes, transforming the delivery of offender management. There was considerable commitment and enthusiasm for IOM at the sites, whilst acknowledging barriers to development such as definition, resourcing, governance and clarity of agency roles. Since the evaluation took place, the political and criminal justice landscape has changed somewhat, supporting a more locally driven approach which can draw on the learning directly from the pioneers which were shaped and delivered locally

Increasing the voluntary and community sector’s involvement in Integrated Offender Management(IOM)

As part of an undertaking to increase voluntary and community sector (VCS) involvement in service delivery, the Home Office set up an initiative to provide small grants to VCS organisations to work with IOM partnerships. The Home Office commissioned an evaluation of the initiative which aimed to: explore the strengths and weaknesses of the funding model; identify perceived barriers and facilitators to voluntary and community sector involvement in IOM; explore how the Home Office might best work with the VCS to encourage and support their capacity to work in partnership with statutory agencies; and identify any implications for the delivery of future similar projects

Process evaluation of Derbyshire Intensive Alternatives to Custody Pilot

The aim of this study was to critically assess the implementation and development of the Intensive Alternatives to Custody (IAC) pilot in Derbyshire. The Ministry of Justice (MoJ) Penal Policy paper (May 2007) outlined the government’s intention to develop higher intensity community orders as an alternative to short-term custody. The IAC Order was subsequently developed and piloted, first in Derbyshire and then in six other areas.* The pilots were centrally funded until March 2011

Improved detection of chemical substances from colorimetric sensor data using probabilistic machine learning

We present a data-driven machine learning approach to detect drug- and explosives-precursors using colorimetric sensor technology for air-sampling. The sensing technology has been developed in the context of the CRIM-TRACK project. At present a fullyintegrated portable prototype for air sampling with disposable sensing chips and automated data acquisition has been developed. The prototype allows for fast, user-friendly sampling, which has made it possible to produce large datasets of colorimetric data for different target analytes in laboratory and simulated real-world application scenarios. To make use of the highly multi-variate data produced from the colorimetric chip a number of machine learning techniques are employed to provide reliable classification of target analytes from confounders found in the air streams. We demonstrate that a data-driven machine learning method using dimensionality reduction in combination with a probabilistic classifier makes it possible to produce informative features and a high detection rate of analytes. Furthermore, the probabilistic machine learning approach provides a means of automatically identifying unreliable measurements that could produce false predictions. The robustness of the colorimetric sensor has been evaluated in a series of experiments focusing on the amphetamine pre-cursor phenylacetone as well as the improvised explosives pre-cursor hydrogen peroxide. The analysis demonstrates that the system is able to detect analytes in clean air and mixed with substances that occur naturally in real-world sampling scenarios. The technology under development in CRIM-TRACK has the potential as an effective tool to control traf- ficking of illegal drugs, explosive detection, or in other law enforcement applications.EU FP7 Grant Agreement Number 31320