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If Only We Knew What We Know

This article contributes to the broader themes surrounding law and technology raised in this symposium by taking a look at lawyering and knowledge management. This topic is presented both as a theory and with a case study. The first part provides a brief summary of the basic lawyering paradigm used in the Lawyering in the Digital Age Clinic at Columbia Law School – that all lawyering activities can be understood within the context of gathering, managing and presenting information. The second category of the paradigm is expanded upon to review the activity of managing knowledge. Then, knowledge management is positioned as the foundation for “reflection in action”, a concept that has been widely recognized within clinical legal education. What follows is to consider the A2J application as an example of an expert system. Then, finally, a brief case study is presented on how the Lawyering in the Digital Age Clinic used the A2J application in conjunction with partners in the New York Court system to address a pressing need on the part of pro se litigants

Client Success or Failure in a Halfway House

Halfway houses today are diverse entities. Seiter, et al. (1977) found that almost 60 percent of the houses in the United States are private nonprofit organizations. One-third were state operations with the remainder being federal, local or private profit organizations. The programs in the houses varied from those providing supervision and custody to those providing a full range of intensive in-house treatments for particular client needs. Some halfway houses handle only particular types of offenders (e.g., drug addicts) while others handle a wide range of offenders. Latessa and Allen (1982) suggest that the sociodemographic and criminal history backgrounds of clients differ depending upon the referral sources to the halfway house. Allen and Seiter (1981) developed three alternative models of halfway houses based on where they fit in the criminal justice system. In the first model, the inmate resides in the halfway house during the initial parole period. The second model covers those situations in which the inmate is transferred to a halfway house before parole is granted. In the third model, the inmates are granted parole and placed in the community on their own. The parolee is placed in the halfway house if problems begin to develop. Latessa and Allen (1982) call for further research on the types of clients in halfway houses and on client risk, their need levels and special problems. This research addresses these issues. This article describes one halfway house, Cope House, in Dayton, Ohio. It is a diversified halfway house which does not fit any of the alternative models suggested by Allen and Seiter (1981). Cope House accepts adult male and female referrals from the Federal Bureau of Prisons, the Department of Corrections of the State of Ohio, the Montgomery County Probation Department, and female referrals only from the City of Dayton Municipal Court. Cope House became co-correctional in January of 1981

Predictors of Success in a Co-Correctional Halfway House: A Discriminant Analysis

Considerable research and debate have focused on the effectiveness of community correctional programs. Much of the research does not address the issue of the effectiveness of programs for persons with different types of problems or criminal histories. This article utilizes discriminant analysis to determine the characteristics of persons most likely to succeed in one halfway house. The results indicate that strong socializing and integrating ties in the community and few previous contacts with the criminal justice system are major predictors of success in a halfway house program. The seven discriminators for females are used to accurately predict 87 percent of the female misdemeanants while the nine discriminators for male felons correctly predict 63 percent of the cases

Air Quality Modelling for Ireland

Air pollution is the primary environmental cause of premature death in the EU (European Commission, 2013) and the most problematic pollutants across Europe have consistently been oxides of nitrogen (e.g. nitrogen dioxide (NO2)), particulate matter (e.g. PM10, PM2.5) and ozone (O3). While measurements form an important aspect of air quality assessment, on their own they are unlikely to be sufficient to provide an accurate spatial and temporal description of the pollutant concentrations for exposure assessment and moreover they cannot provide information regarding future air quality. Annex XVI of 2008/50/EC requires member states to “ensure that up to date information on ambient concentrations of the pollutants covered” by the Directive are “made available to the public”. This information must include actual or predicted exceedances of alert and information thresholds and a forecast for the following day of which a model is an integral part. As a result, air quality models are increasingly required for public information, air quality management and research purposes. The primary objectives of this research fellowship were to develop a calibrated air quality forecast model for Ireland capable of predicting the Air Quality Index for Health (AQIH) in each of the air quality zones in Ireland and to model the spatial variation in concentrations on a national scale. This research project has produced three different models for NO2, PM10, PM2.5, O3 and SO2, all of which are available for further use. These are: A hybrid point wise 48 hour forecast model; Spatial model (WS-LUR) to produce annual mean maps of air pollution on a national scale; Temporal WS-LUR model. A comprehensive review of modelling systems carried out at the outset of this research fellowship, together with consideration for key EPA objectives, informed the direction of model development. This review is available as a separate EPA report. A priority within the EPA was to produce air quality forecasts based on the AQIH. The AQIH is based on point wise measurements and in order to extrapolate these measurements to the future, statistical modelling was deemed the most suitable. The advantages of this approach were that it could be developed from first principles specific to the area of interest and completely (avoiding any reliance on a third party to supply the model or apply licensing restrictions) and the associated speed of forecast computation. Forecasts are only useful if they can be computed and made available to the public relatively quickly. The accuracy of such methods also tends to be high and of low bias as they are developed site-specifically unlike large scale deterministic models that are often developed and tested in vastly differing domains. In particular, this method was capable of producing accurate point wise forecasts without the need for a detailed emissions inventory. At the project outset, the emissions inventory was not of sufficient spatial resolution to make realistic point wise forecasts in all air quality zones by deterministic means and it would have been an inefficient use of resources to base the development of forecasts on what was currently available. Initial model development proceeded using time series analysis in conjunction with non-parametric kernel regression, with local meteorological parameters as predictor variables. A model validation study found that this technique produced accurate forecast of ozone and SO2 but had a tendency to under predict peak NO2 and PM10/2.5 concentrations. An analysis of air mass history using the HYSPLIT model was carried out which revealed certain air masses (primarily easterly and re-circulated air) were responsible for most incidence of elevated concentrations. The results of this study were used to develop a HYSPLIT add-on for the forecast model which operates by forecasting air mass history in real time and invoking a different forecasting methodology depending on the region of origin of the air. The ability of the hybrid point wise model to predict daily maximum hourly NO2, SO2, 8 hourly ozone and daily average PM10 and PM2.5 was demonstrated by comparing a full year of modelled data with measured data at each of the AQIH sites. Index of agreement values ranged from a low of 0.80 for SO2 to 0.88 for NO2 and ozone, while correlation coefficients ranged from a low of 0.69 for SO2 to 0.82 for NO2. Full results of this validation study are contained in a separate report. In order to provide detail on the spatial variation of concentration levels across the country, land use regression (LUR) was recommended in the model review as the most suitable technique. This technique uses surrogate spatial indicators to explain the variation in concentration levels between monitoring points. Land cover data (CORINE), DTM output, road density information and population data are all factors that influence concentration levels and data that were broadly available. In contrast to most LUR studies, circular buffers were not used in the determination of spatial predictor variables. Rather, a novel sector based approach (WS-LUR) was adopted whereby variables were calculated within 8 pre-defined sectors representing the major wind directions around each monitoring site. This approach had a dual purpose. Firstly, it accounts for the direction of influence of emission sources on air quality in a given location. Traditional LUR assumes equal influence of emissions in the area surrounding a monitoring site regardless of wind direction. This approximation may be reasonable in highly urbanised areas where emissions sources are relatively uniform in the surrounding region. However, in this study the regression was applied on a national scale and prevailing winds coupled with clear directional influenced at air quality monitoring sites meant that WS-LUR is a superior option. The second advantage of this methodology is that it increases the effective number of data points available for the regression analysis, resulting in a more robust final equation. In conjunction with research project (2013-EH-FS-7), a set of annual mean maps within a geographic information system (GIS) environment were created and validated for each of NO2, PM10, PM2.5, O3 and SO2. These provide a highly relevant source of information regarding spatial variation in concentration levels on a national scale which can be used not only for exposure studies and general air quality assessment, but also as a tool to correlate emission sources and surrogates with air quality. A temporal WS-LUR model was developed for NO2, Ozone and PM10 by including hourly meteorological data in conjunction with pre-specified spatial data as predictor variables. This model has the potential to provide fast, efficient national air quality forecast maps for Ireland with minimal computational requirements. This project has achieved key EPA objectives and has produced a fully automated and operational air quality model which produced twice-daily forecasts of the AQIH in each air quality zone in Ireland. The stepwise approach chosen for model development allowed deliverables prior to completion of the project while minimising associated risks. The models developed as part of this fellowship form solid building blocks on which future air quality modelling studies in Ireland can be based

Nonprofit Power: Engaging Voters for a More Inclusive Democracy

Voter turnout gaps along the lines of race, age, income, and education level distort our democracy at the policy debates that flow from it. These gaps are present even in the most voter-friendly states as they are a reflection of who is and is not contacted about voting. To close the gaps and foster a more representative electorate, we need to build civic ecosystems to engage currently underrepresented voices.As this report shows, nonprofit service providers are uniquely well-positioned to foster a more inclusive democracy. Nonprofits like food pantries, health centers, housing clinics, and family service agencies reach demographic groups underrepresented at the polls. Furthermore, the nonpartisan voter engagement conducted by these nonprofits has its biggest turnout impact among these target groups contributing to a more representative electorate

Bullying in Scotland 2014

The primary aim of this piece of research was to obtain a picture of how children and young people are experiencing bullying in Scotland in 2014. This research was designed to: • Identify the types of bullying that is experienced by children and young people • Give a clear picture of where bullying happens and where online and offline/face to face experiences differ or coalesce • Identify from children and young people’s own experience what they feel works and what is less helpful • Identify where children and young people go online and what technology they use to get ther