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After a synopsis of the historical background of SVA, I describe the four phases (stages) it consists of I then review the available SVA research The core phase of SVA is Criteria-Based Content Analysis (Berliner & Conte, 1993), a list of 19 criteria thought to be more present in truthful than in false accounts Most of the research has concentrated on this part of SVA I discuss how accurate experts are in detecting truths and lies when they apply Criteria-Based Content Analysis (CBCA) and the extent to which two experts who analyse the same statement obtain the same CBCA scores (so-called inter-rater agreement) Research reveals that truths and lies can be detected above the level of chance with the CBCA method, but errors are also made.

I then discuss research involving the Validity Checklist (another phase of SVA), a list of 11 issues other than deception thought to have an impact on CBCA scores Validity Checklist research includes studies examining the effects of the interviewing style, the interviewee s age, and coaching of the interviewee on CBCA scores I will show that all three issues impact upon CBCA scores In this chapter I review the several problems that are associated with applying the Validity Checklist, which include dif culty in identifying and measuring some issues that may in uence CBCA scores, and dif culty in determining the exact impact of these issues on CBCA scores I will also question the justi cation of some of the issues that are included in the Validity Checklist and argue that some issues that may in uence CBCA scores are not included in the Validity Checklist.

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I nally discuss the implications of the research ndings for the use of SVA in criminal investigations I will argue that SVA evaluations are useful in criminal investigations but are not accurate enough to be admitted as expert scienti c evidence in criminal courts..

2.28 2.23 2.18 2.13 2.08

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The map-matched GPS positions output from method 2 used Ordnance Survey (OS) road centre-line data, OS DTM data for height aiding RRF for correct road selection and MDOP to correct for along-track errors. To correctly display the vehicle positions on OS large-scale mapping, all resultant latitude, longitude and height coordinates from the three solutions were transformed to OSGB36 (Ordnance Survey of Great Britain, 1936) National Grid, with a nominal transformation accuracy of 20 cm (OS, 1999). At each epoch, where all three positions were available, the difference in position between RAW and RTK and map-matched GPS and RTK were calculated (position error). A summary of the results is given in Table 6.2. It is also of interest to note that the maximum position errors were 177 m for the RAW data and 76 m for the map-matched GPS data (43 m for 95%). Mean error of position has been reduced from 36 m to 13 m over a total of 1112 vehicle positions. The variation in both cross-track and along-track error is also much reduced. It can be seen that the map-matched GPS described in this chapter provides a much improved accuracy of position, particularly if the worst 5% of position errors are removed. In fact, MDOP can be used to identify (predict) where on the route the error vector will be least accurately modelled. Inspection of the estimated residuals tells us when we have a poor error vector. If the residuals are low, then we can reject a road segment with the RRF. All the really large errors occur when the vehicle is stationary or almost stationary such as at a road junction, e.g. approximately at GPS seconds 482000, 482400 (both at roundabouts) and at 482150 (motorway slip road). These two

public void setSessionContext(SessionContext context) { this.context = context; } public void afterBegin() { System.out.println("afterBegin()"); try { cartItems = new List(); } catch (Exception ex) { throw new EJBException("afterBegin Exception: " + ex.getMessage()); } } public void beforeCompletion() { System.out.println("beforeCompletion()"); } public void afterCompletion(boolean committed) { System.out.println("afterCompletion: " + committed); if (committed == false) { try { cartItems.clear(); } catch (Exception ex) { throw new EJBException("afterCompletion SQLException: " + ex.getMessage()); } } } public ShoppingCartBean() {}

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