A Statistical Validation of the Individuality and Repeatability of Striated Tool Marks: Screwdrivers and Tongue and Groove Pliers*

Abstract: Tool mark identification relies on the premise that microscopic imperfections on a tool’s working surface are sufficiently unique and faithfully transferred to enable a one‐to‐one association between a tool and the tool marks it creates. This paper presents a study undertaken to assess the validity of this premise. As part of this study sets of striated tool marks were created under different conditions and on different media. The topography of these tool marks was acquired and the degree of similarity between them was quantified using well‐defined metrics. An analysis of the resulting matching and nonmatching similarity distributions shows nearly error‐free identification under most conditions. These results provide substantial support for the validity of the premise of tool mark identification. Because the approach taken in this study relies on a quantifiable similarity metric, the results have greater repeatability and objectivity than those obtained using less precise measures of similarity.     

Validation of Tool Mark Comparisons Obtained Using a Quantitative,Comparative, Statistical Algorithm

Abstract: A statistical analysis and computational algorithm for comparing pairs of tool marks via profilometry data is described. Empirical validation of the method is established through experiments based on tool marks made at selected fixed angles from 50 sequentially manufactured screwdriver tips. Results obtained from three different comparison scenarios are presented and are in agreement with experiential knowledge possessed by practicing examiners. Further comparisons between scores produced by the algorithm and visual assessments of the same tool mark pairs by professional tool mark examiners in a blind study in general show good agreement between the algorithm and human experts. In specific instances where the algorithm had difficulty in assessing a particular comparison pair, results obtained during the collaborative study with professional examiners suggest ways in which algorithm performance may be improved. It is concluded that the addition of contextual information when inputting data into the algorithm should result in better performance.     

Fingermark Evidence Evaluation Based on Automated Fingerprint Identification System Matching Scores: The Effect of Different Types of Conditioning on Likelihood Ratios

In recent studies, the evidential value of the similarity of minutiae configurations of fingermarks and fingerprints, for example expressed by automated fingerprint identification systems (AFIS), is determined by likelihood ratios (LRs). The paper explores whether there is an effect on LRs if conditioning takes place on specified fingers, fingerprints, or fingermarks under competing hypotheses: In addition, an approach is explored where conditioning is asymmetric. Comparisons between fingerprints and simulated fingermarks with eight minutiae are performed to produce similarity score distributions for each type of conditioning, given a fixed AFIS matching algorithm. Both similarity scores and LRs are significantly different if the conditioning changes. Given a common‐source scenario, “LRs” resulting from asymmetric conditioning are on average higher. The difference may reach a factor of 2000. As conditioning on a suspect's finger(print) is labor‐intensive and requires a cooperating suspect, it is recommended to just condition on the number of minutiae in the fingermark.     

The Class Characteristic Mark of the H&M Mul‐T‐Lock Picking Tool in Toolmarks Examination

Mul‐T‐Lock is a high security lock cylinder distinguished by the use of a telescoping “pin‐in‐pin”—tumbler design. Picking the Mul‐T‐Lock cylinder with a traditional picking tool is highly complicated because it can get stuck between the inner and outer pins. The H&M Mul‐T‐Lock picking tool was designed to overcome this problem and facilitate the picking of the “pin‐in‐pin” cylinder. The purpose of this research is to determine whether H&M Mul‐T‐Lock picking tool leaves class characteristic mark and whether it can be distinguished from traditional picking tools marks and from regular key marks. It also describes and determines the class characteristic mark left on telescopic pins, its origin, recurrence, and its benefit to the toolmarks examiner. When receiving a Mul‐T‐Lock from a crime scene, a toolmarks examiner can quickly determine whether or not it was picked by an H&M Mul‐T‐Lock picking tool by noticing the class characteristic mark which this typical tool leaves.     

A Fully Automatic Method for Comparing Cartridge Case Images,

When a gun is fired, it leaves marks on cartridge cases that are thought to be unique to the gun. In current practice, firearms examiners inspect cartridge cases for “sufficient agreement,” in which case they conclude that they come from the same gun, testifying in courts as such. A 2016 President's Council of Advisors on Science and Technology report questioned the scientific validity of such analysis (President's Committee of Advisors on Science and Technology, Washington, DC, Executive Office of the President). One recommendation was to convert firearms analysis to an objective method. We propose a fully automated, open‐source method for comparing breechface marks on cartridge cases using 2D optical images. We improve on existing methodology by automating the selection of marks, and removing the effects of circular symmetry. We propose an empirical computation of a “random match probability” given a known database, which can be used to quantify the weight of evidence. We demonstrate an improvement in accuracy on images from controlled test fires.     

Score based procedures for the calculation of forensic likelihood ratios – Scores should take account of both similarity and typicality

Score based procedures for the calculation of forensic likelihood ratios are popular across different branches of forensic science. They have two stages, first a function or model which takes measured features from known-source and questioned-source pairs as input and calculates scores as output, then a subsequent model which converts scores to likelihood ratios. We demonstrate that scores which are purely measures of similarity are not appropriate for calculating forensically interpretable likelihood ratios. In addition to taking account of similarity between the questioned-origin specimen and the known-origin sample, scores must also take account of the typicality of the questioned-origin specimen with respect to a sample of the relevant population specified by the defence hypothesis. We use Monte Carlo simulations to compare the output of three score based procedures with reference likelihood ratio values calculated directly from the fully specified Monte Carlo distributions. The three types of scores compared are: 1. non-anchored similarity-only scores; 2. non-anchored similarity and typicality scores; and 3. known-source anchored same-origin scores and questioned-source anchored different-origin scores. We also make a comparison with the performance of a procedure using a dichotomous “match”/“non-match” similarity score, and compare the performance of 1 and 2 on real data.     

Optimization of a Statistical Algorithm for Objective Comparison of Toolmarks

Due to historical legal challenges, there is a driving force for the development of objective methods of forensic toolmark identification. This study utilizes an algorithm to separate matching and nonmatching shear cut toolmarks created using fifty sequentially manufactured pliers. Unlike previously analyzed striated screwdriver marks, shear cut marks contain discontinuous groups of striations, posing a more difficult test of algorithm applicability. The algorithm compares correlation between optical 3D toolmark topography data, producing a Wilcoxon rank sum test statistic. Relative magnitude of this metric separates the matching and nonmatching toolmarks. Results show a high degree of statistical separation between matching and nonmatching distributions. Further separation is achieved with optimized input parameters and implementation of a “leash” preventing a previous source of outliers—however complete statistical separation was not achieved. This paper represents further development of objective methods of toolmark identification and further validation of the assumption that toolmarks are identifiably unique.     

Valence as a measurement of the effectiveness of probation officer-client relationship

Probationers are expected to be socialized or resocialized through the assumed “primary relationship” which is supposed to occur by randomly assigning probationers to officers. A Likert-type scale was developed to assess the necessary ingredient of a primary group—relationship valence—and was administered to the clients of an urban probation agency. The assumption was supported. However, evidence suggests possible improvement through planned matching of clients to officers based on client and officer personality profiles. The Relationship Valence (RV) Scale facilitates this matching as well as assists in officer development and program evaluation.The RV Scale can be used as a research instrument to enhance a greater understanding of clients by comparing high scorers with low scorers and also to enhance a greater understanding of officers by comparing officers who elicit high scores with those who elicit low scores. In addition, the RV Scale is a tool to investigate the relationship between “primary groupness” with client attitude change.     

Expertise in Fingerprint Identification

Although fingerprint experts have presented evidence in criminal courts for more than a century, there have been few scientific investigations of the human capacity to discriminate these patterns. A recent latent print matching experiment shows that qualified, court‐practicing fingerprint experts are exceedingly accurate (and more conservative) compared with novices, but they do make errors. Here, a rationale for the design of this experiment is provided. We argue that fidelity, generalizability, and control must be balanced to answer important research questions; that the proficiency and competence of fingerprint examiners are best determined when experiments include highly similar print pairs, in a signal detection paradigm, where the ground truth is known; and that inferring from this experiment the statement “The error rate of fingerprint identification is 0.68%” would be unjustified. In closing, the ramifications of these findings for the future psychological study of forensic expertise and the implications for expert testimony and public policy are considered.     

Legal Power: The Basic Definition

The concept of legal power (often called legal competence) is important in the law since, with regard to actions having legal effect, the “exercise of legal power” delimits those actions for which manifestation of intention to achieve a legal effect is essential for the effect to ensue. The paper proposes a definition that captures this feature of legal power and marks it off from “direct effect,” as well as from permissibility and practical ability to achieve the legal effect. This analysis of power is limited to the “immediate” legal power of a physical person characterized by the power‐holder achieving a legal result by the power‐holder's own behaviour (not by representatives acting on behalf of the power‐holder). It is argued that in the literature on power the concept of legal power is frequently construed in such a way that it becomes either too broad or too narrow.     

A Set of Handwriting Features for Use in Automated Writer Identification,

A writer's biometric identity can be characterized through the distribution of physical feature measurements (“writer's profile”); a graph‐based system that facilitates the quantification of these features is described. To accomplish this quantification, handwriting is segmented into basic graphical forms (“graphemes”), which are “skeletonized” to yield the graphical topology of the handwritten segment. The graph‐based matching algorithm compares the graphemes first by their graphical topology and then by their geometric features. Graphs derived from known writers can be compared against graphs extracted from unknown writings. The process is computationally intensive and relies heavily upon statistical pattern recognition algorithms. This article focuses on the quantification of these physical features and the construction of the associated pattern recognition methods for using the features to discriminate among writers. The graph‐based system described in this article has been implemented in a highly accurate and approximately language‐independent biometric recognition system of writers of cursive documents.     

Where the Margins Meet: A Demographic Assessment of Transgender Inmates in Men’s Prisons

Drawing on official data and original interview data on 315 transgender inmates in California prisons for men, this research provides the first empirical portrayal of a prison population in California that is unique by virtue of being both transgender and incarcerated. Situated at the nexus of intersecting marginalities, transgender inmates fare far worse on standard demographic and health measures than their non‐transgender counterparts in the US population, the California population, the US prison population, and the California prison population. With the possible exceptions of partnership status and educational attainment, these factors combine to reveal that transgender inmates are marginalized in heretofore undocumented ways. At a time in which an evidence‐based approach to corrections is increasingly embraced by corrections officials in the US, this article provides the first systematic profile of transgender prisoners. It reveals they can be regarded as a special population that, from a policy point of view, raises what Minow calls “the dilemma of difference”.     

Development of a Mobile Toolmark Characterization/Comparison System

Since the development of the striagraph, various attempts have been made to enhance forensic investigation through the use of measuring and imaging equipment. This study describes the development of a prototype system employing an easy‐to‐use software interface designed to provide forensic examiners with the ability to measure topography of a toolmarked surface and then conduct various comparisons using a statistical algorithm. Acquisition of the data is carried out using a portable 3D optical profilometer, and comparison of the resulting data files is made using software named “MANTIS” (Mark and Tool Inspection Suite). The system has been tested on laboratory‐produced markings that include fully striated marks (e.g., screwdriver markings), quasistriated markings produced by shear‐cut pliers, impression marks left by chisels, rifling marks on bullets, and cut marks produced by knives. Using the system, an examiner has the potential to (i) visually compare two toolmarked surfaces in a manner similar to a comparison microscope and (ii) use the quantitative information embedded within the acquired data to obtain an objective statistical comparison of the data files. This study shows that, based on the results from laboratory samples, the system has great potential for aiding examiners in conducting comparisons of toolmarks.     

A Novel Forensic Tool for the Characterization and Comparison of Printing Ink Evidence: Development and Evaluation of a Searchable Database Using Data Fusion of Spectrochemical Methods

A searchable printing ink database was designed and validated as a tool to improve the chemical information gathered from the analysis of ink evidence. The database contains 319 samples from printing sources that represent some of the global diversity in toner, inkjet, offset, and intaglio inks. Five analytical methods were used to generate data to populate the searchable database including FTIR, SEM‐EDS, LA‐ICP‐MS, DART‐MS, and Py‐GC‐MS. The search algorithm based on partial least‐squares discriminant analysis generates a similarity “score” used for the association between similar samples. The performance of a particular analytical method to associate similar inks was found to be dependent on the ink type with LA‐ICP‐MS performing best, followed by SEM‐EDS and DART‐MS methods, while FTIR and Py‐GC‐MS were less useful in association but were still useful for classification purposes. Data fusion of data collected from two complementary methods (i.e., LA‐ICP‐MS and DART‐MS) improves the classification and association of similar inks.     

Three dimensional visualization and comparison of impressions on fired bullets

Currently, optical devices, such as microscopes and CCD cameras, are utilized for identification of bullets and tool marks in the field of forensic science. While these optical methods are easily manageable and effective, they are under great influence of illumination condition. In other words, appearances of striations through these optical devices have possibility to be changed by lighting condition. Besides these appearance-based approaches, we can utilize three dimensional (3D) geometric data of tool marks that are free from lighting condition. In this study, we focused on 3D geometric data of landmark impressions on fired bullets for identification. We obtained the 3D surface data of tool marks by a confocal microscope and reconstructed virtual impressions on a PC monitor from the geometric data. Furthermore, the 3D data are exploited to numerical matching of two surface shapes. We also visualized the difference of two shapes. In order to do this, two surface models are aligned automatically. In this process, pairings of correspondent points on both surfaces are determined. Distance analysis between these pairs leads to a shape comparison. Since comparison results are visualized, they are intuitive and easily perceptive.     

IN SEARCH OF A DEPENDENT VARIABLE: COMMENT ON AVAKAME, 1998*

In a previous Criminology article, Avakame (1998) applies hierarchical linear modeling (HLM) techniques to Supplementary Homicide Reports (SHR) to disentangle individual‐ and aggregate‐level factors associated with offending. A close reading of his analysis reveals serious flaws in the dependent variable, which renders the results meaningless. Although it is ambiguous whether Avakame intended to model homicide “risk” or “frequency,” either is problematic. “Homicide frequency” has no logical connection to the individual‐level predictors; “homicide risk” is constant in SHR data, which makes the analysis impossible. In detailing these problems, we spell out the logical data requirements and offer sound empirical examples for an HLM analysis.     

Students’ self‐assessment in law

Abstract

THIS STUDY is about second‐year students’ self‐assessment in law. Its aims were to test how closely the assessment by the students of their own work matched that of their tutor, to give the students an insight into the “marking process” (which research has shown improves learning), to help the students excel in their examination, to find out whether the male students did better than the female students in their assignment and subsequent examination, and to see how far staff time could be saved. The following were found: there was a high level of agreement between the students’ own marks and those of the tutor, there was generally no significant over‐estimation of assignment marks by the students, the participants did better than the non‐participants in the subsequent examination, the female students did much better than the male students in the assignments as well as the examination (although the participants in general did not do better in their examination than they did in their assignment) and considerable staff time was saved. The findings are discussed in the light of previous research.     

Revisiting the Use of Propensity Score Matching to Understand the Relationship between Gang Membership and Violent Victimization: A Cautionary Note

Previous research has consistently reported that gang members are more likely to experience violent victimization compared to non‐gang members. Recently, however, a study challenged this conventional wisdom using the Gang Resistance Education and Training (GREAT) data. Employing propensity score matching (PSM), this study reported no significant differences in violent victimization between gang and non‐gang members. Upon closer examination of the GREAT data and the PSM process used in this study, we note several theoretical, methodological, and statistical concerns. We reanalyze the GREAT data using both negative binomial regression and PSM. We find that self‐reported gang members were significantly more likely to report subsequent violent victimization compared to non‐gang members. Although contrary to this previous study, our findings are consistent with the bulk of previous empirical research and widely held beliefs about the relationship between gang membership and violent victimization.     

Classification of the Y-haplogroup distributions of Western Eurasian populations using a self-learning algorithm

The understanding of historical relationship between populations is a core aspect of human population history studies. We have compared the frequency of 18 different Y-SNP haplogroups in 90 Western Eurasian populations. Classification of haplogroup distribution vectors using a new self-learning classification algorithm so called “self-organizing cloud (SOC)” proved to be an effective tool to identify population groups, which share common paternal genetic features. By means of the algorithm, we have determined 10 different classes of populations based on the similarity of haplogroup composition. The analysis showed that paternal genetic markers tend to reflect geographical proximity of populations better than linguistic relationship, although certain Y-SNP haplogroups have relatively good correlation with specific language families. These observations are based on the comparative analysis of the Hg distributions of contemporary populations may reflect demographic history of them in the past.