Measuring MAN (incorporating JRAI): Computational anthropological analysis and quantitative speculation

Police reports made following attendance at various events (e.g., crashes, domestic violence, theft) often contain rich contextual details including indicators of mental health issues or abuse types, and persons/entities involved and their relationships, which are not typically captured in structured administrative data, interviews or official statistics. However, the sheer volume of information along with strict data access protocols render manual analysis impractical. Computational text analysis methods offer a feasible and effective approach to automatically process this underutilized data source. This article is an overview of studies using computational text analysis (e.g., text mining, natural language processing (NLP)), on unstructured police data, serving as a guide for researchers interested in employing similar methodologies. This scoping review was conducted in accordance with the PRISMA-SCR guidelines, following the two screening processes (title/abstract and full text screening) and the development of a pre-defined protocol. A search was conducted across seven electronic databases (ProQuest, IEEE Xplore, Scopus, PubMed, Web of Science, Criminal Justice Abstracts, Google Scholar) covering the past 20 years. A total of 5426 records were identified. After removing duplicate entries and screening titles/abstracts and full-text publications, 61 studies met the inclusion criteria. Included studies were published between 2004 and 2024, with most from the United States, Australia and the Netherlands. Most studies used opensource tools: Bidirectional Encoder Representations from Transformers (BERT), natural language tool kit (NLTK), scikit-learn, or General Architecture for Text Engineering (GATE) to analyze unstructured police data. Our review indicates applications of computational text analysis on unstructured police data have moderate to high performance. Common limitations included variable data quality, with reliability depending on the level of detail provided by the police report’s author, and failure to report ethical implications or methodological limitations. Computational text analysis can extract key information from unstructured police data. However, future research should clearly report ethics approvals and implications, and methodological limitations. Establishing a structured data-sharing framework between law enforcement and researchers is also crucial to facilitate access and support high quality, impactful research in this field.

SignificanceWe introduce an approach to identify latent topics in large-scale text data. Our approach integrates two prominent methods of computational text analysis: topic modeling and word embedding. We apply our approach to written narratives of violent death (e.g., suicides and homicides) in the National Violent Death Reporting System (NVDRS). Many of our topics reveal aspects of violent death not captured in existing classification schemes. We also extract gender bias in the topics themselves (e.g., a topic about long guns is particularly masculine). Our findings suggest new lines of research that could contribute to reducing suicides or homicides. Our methods are broadly applicable to text data and can unlock similar information in other administrative databases.

This paper proposes a new approach to reading the ‘Confessional Poetry’ of Sylvia Plath and Anne Sexton by combining quantitative text analysis methods with a qualitative literary interpretation. We investigate the meanings of stylistic differences between Plath’s and Sexton’s poems and highlight their differences in using pronouns and related sentiment words. Our data-focused research demonstrates that Plath employs rather fragmented and schizophrenic styles, as well as poetic voices with word choices that present dynamic and volatile emotions. On the other hand, Sexton’s poetic voice is causal, homogeneous, and relatively stable, focusing a lot on domestic issues and the symbolic function of father. To illustrate this, three computational methods are used: (1) linking pronoun uses in each poet’s work to sentiment words (Figs 1 and 2), (2) a topic-model analysis to compare Plath’s and Sexton’s works to see how the themes of poetry are manifested by the different patterns of word use, the so-called topics, and their co-occurrences in the poems (Figs 3 and 4), and (3) a diachronic analysis of each poet’s works (using the topic model) to identify the thematic changes (Figs 5 and 6). The methods commonly testify one of the widely accepted ideas regarding Plath’s and Sexton’s confessional poems. Plath often breaks with the boundary of the ego, I, through complex reciprocity between I and other pronoun groups, while Sexton evokes the presence of the poet through more stable and less kinetic voices of the authorial I. Plath’s and Sexton’s works have long been regarded as the passive products of biological reductionism due to the traditional way of understanding the confessional genre. The aim of this paper, however, is to redefine the meaning of confessional genre, proposing that the truthfulness of the poetic voice I cannot be seperated from the stylistic issues in poetic language.

In recent decades, the amount of text available for organizational science research has grown tremendously. Despite the availability of text and advances in text analysis methods, many of these techniques remain largely segmented by discipline. Moreover, there is an increasing number of open-source tools (R, Python) for text analysis, yet these tools are not easily taken advantage of by social science researchers who likely have limited programming knowledge and exposure to computational methods. In this article, we compare quantitative and qualitative text analysis methods used across social sciences. We describe basic terminology and the overlooked, but critically important, steps in pre-processing raw text (e.g., selection of stop words; stemming). Next, we provide an exploratory analysis of open-ended responses from a prototypical survey dataset using topic modeling with R. We provide a list of best practice recommendations for text analysis focused on (1) hypothesis and question formation, (2) design and data collection, (3) data pre-processing, and (4) topic modeling. We also discuss the creation of scale scores for more traditional correlation and regression analyses. All the data are available in an online repository for the interested reader to practice with, along with a reference list for additional reading, an R markdown file, and an open source interactive topic model tool (topicApp; see https://github.com/wesslen/topicApp, https://github.com/wesslen/text-analysis-org-science, https:// dataverse.unc.edu/dataset.xhtml?persistentId=doi:10.15139/S3/R4W7ZS).KeywordsText analysisTopic modelingStructural topic modelingThematic analysisContent-analysisDictionary analysisNatural language processing

This article presents a teaching concept for the professionalization of prospective teachers. The concept is focused on the synchronization of basic digital skills in dealing with different methods of computational text analysis – which also includes literary studies-oriented corpus analyses – with the development of didactic skills. After a brief introduction, the first part of the article highlights the motivation and relevance of bridging the gap between digital humanities and teacher training. The second part focuses on conceptual foundations, areas of knowledge, and the rough outline of the seminar “Digital Literary Studies and Literary Didactics” (held in the Winter semester of Study year 2023/24 and the Winter semester of 2024/25 at the Institute of German Studies at the University of Hamburg) with special consideration of the session on corpus analysis. Finally, the results, opportunities and limitations of the teaching concept are examined.

Stakeholder concerns of air pollution in Hong Kong and policy implications: A big-data computational text analysis approach

The adoption of soil conservation practices by farmers offers the potential to greatly improve soil health and water quality at large geographic scales. In considering the potential benefits of soil conservation practices to improve ecological outcomes on farms, it is important to ascertain where farmers get their information about soil conservation and what type of information they are exposed to and by whom. One primary way that farmers learn about soil conservation practices is via agricultural trade publications (ATPs). We conducted a content analysis using a computational text analysis method to analyze all the online soil conservation coverage from four influential ATPs in Wisconsin. We focused on 10 different soil conservation practices and found that the most frequently covered soil conservation practices were tillage, manure, and grazing. Additionally, we analyzed the thematic categories for how each soil conservation practice was covered in terms of agricultural, environmental and economic benefits. Generally, articles tended to mention environmental and economic benefits more than agricultural benefits across all soil conservation practices. We also unpacked the subcategories of environmental benefits using cover crops practice as an example to demonstrate how it was covered in terms of subcategories such as biodiversity, sustainability, climate change, water quality, and soil health. Our analysis also looked at how agricultural technology was featured in the stories about soil conservation and found that this category was regularly mentioned for each practice. Finally, we examined the message sources for stories on soil conservation and found that extension and the federal government were the most the frequently cited entities. We also discussed how this form of computational content analysis can provide longitudinal insights about trends in a particular soil conservation practice like cover crops, which showed a clear upward trend in coverage in ATPs for the time period studied. These nuanced content analyses provide insights into what types of thematic categories are featured about soil conservation practices covered in ATPs in Wisconsin. Advocates of soil conservation practices can use our results to determine if some practices could benefit from more attention in ATPs as well as which benefits and themes have received more media coverage. Additionally, stakeholders from entities that serve as different message sources can determine how their organizations are doing as the spokespeople for the soil conservation practices being advocated.

Current research in computer vision highlights the potential of using computational methods to analyze and access large datasets of real images and videos, performing tasks such as object detection or finding visual similarities. This essay describes how the application of these computational methods to digital art data transforms information inherent in images into new knowledge. We support this claim by presenting various research examples in the field of digital art history, which utilize computational methods for art analysis. We argue that in order to create new knowledge, we must involve transformative processes — from analog to digital data and digital to computational methods. Traditional methods used in art history to access datasets, link or edit images provide suggestions and validations for current practices, but are not sufficient role models for the processing of digital data because they were developed under varying technological conditions and standards and within a different historical context. Aby Warburg’s (1866-1929) Mnemosyne Atlas, which aimed to visualize visual continuities from antiquity to the Renaissance, is one method often cited in the context of digital humanities. We argue that the characteristics of digital data, for example being reproducible or modifiable, require innovative computer methods that do not have a direct analog counterpart. Our argument is based on the success of current projects and personal experience: the authors have expertise in computer vision and art history. They have created and applied computational methods to analyze art data and are thus able to identify shortcomings of traditional approaches and suggest possible solutions. Eventually this essay presents solutions, which demonstrate the great potential of computational methods for a data analysis: approaches enable an easy and explorative access to digital image collections through visualization techniques or an automatic object search. Computational methods establish links between thousands of images, thereby identifying the adaptation of specific motifs or styles by artists over time, or enable a conspicuous editing of images, thus providing new insights for art history unobtainable with analog methods.

A computational approach is essential whenever the complexity of the process under study is such that direct theoretical or experimental approaches are not viable. This is the case for protein folding, for which a significant amount of data are being collected. This paper reports on the essential role of in silico methods and the unprecedented interplay of computational and theoretical approaches, which is a defining point of the interdisciplinary investigations of the protein folding process. Besides giving an overview of the available computational methods and tools, we argue that computation plays not merely an ancillary role but has a more constructive function in that computational work may precede theory and experiments. More precisely, computation can provide the primary conceptual clues to inspire subsequent theoretical and experimental work even in a case where no preexisting evidence or theoretical frameworks are available. This is cogently manifested in the application of machine learning methods to come to grips with the folding dynamics. These close relationships suggested complementing the review of computational methods within the appropriate theoretical context to provide a self-contained outlook of the basic concepts that have converged into a unified description of folding and have grown in a synergic relationship with their computational counterpart. Finally, the advantages and limitations of current computational methodologies are discussed to show how the smart analysis of large amounts of data and the development of more effective algorithms can improve our understanding of protein folding.

In this paper is study the performance of heat exchangers by using computational analysis method. In past decade’s years, many researchers had discovered that nanofluids have a brilliant efficient in the heat transfer process. They believes that applied the nanofluids in the heat transfer equipment would bring a better enhancement compare with the conventional cooling fluids. In the literature journals, the authors have specified focused on the thermal conductivity, specific heat, density, and viscosity of nanofluids. They believes these are the most significant factors of the working fluids that would influence the efficiency of heat exchanger. In this study, an imitated real-life marine transport used of shell and tube heat exchanger to be analyzed. The shell and tube sides of heat exchanger will be filled with Water or nanofluids and Water or seaWater in respectively. The nanofluids will be assumed as single-phase nanofluids with variant of volume fraction of copper and alumina. The computational result of Water at the shell and tube sides will be used as validation of data compared with the technical performance specification given by the supplier. Further, the efficiency of different volume fraction of nanofluids and the optimum of volume fraction of nanofluids in this system would be discussed in the simulation results.

Field emergence poses an intriguing problem for institutional theorists. New issue fields often arise at the intersection of different sectors, amidst extant structures of meanings and actors. Such nascent fields are fragmented and lack clear guides for action; making it unclear how they ever coalesce. The authors propose that provisional social structures provide actors with macrosocial presuppositions that shape ongoing field-configuration; bootstrapping the field. The authors explore this empirically in the context of social impact investing in the UK, 2000–2013, a period in which this field moved from clear fragmentation to relative alignment. The authors combine different computational text analysis methods, and data from an extensive field-level study, to uncover meaningful patterns of interaction and structuration. Our results show that across various periods, different types of actors were linked together in discourse through “actor–meaning couplets.” These emergent couplings of actors and meanings provided actors with social cues, or macrofoundations, which guided their local activities. The authors thus theorize a recursive, co-constitutive process: as punctuated moments of interaction generate provisional structures of actor–meaning couplets, which then cue actors as they navigate and constitute the emerging field. Our model re-energizes the core tenets of new structuralism and contributes to current debates about institutional emergence and change.

Victim participation and elaborate outreach programs are becoming increasingly important features of international and hybrid criminal courts. The aspiration is that these programs will increase the local relevance of these courts' work, enhance the sustainability of justice processes after international actors leave, and empower victims en passant. In the last decade, considerable attention and resources have been dedicated to the development of state-of-the-art outreach and participation programs, resulting in exciting new engagement models that are then, on average, evaluated in legal and technical terms. So far, however, little attention has been paid to the more long-term and indirect effects of exposure to certain – unidimensional and hierarchically organized – narratives about justice. In a first step to systematically analyze this question, this article maps the discursive priorities and structure of one of the tribunals (Cambodia) where victim participation and outreach played a central role. The article uses innovative large-n qualitative computational text analysis methods and raises critical questions about (a) the disconnect between prosecutorial prioritization strategies and popular priorities for the justice process, (b) the success of outreach programs in bringing a locally relevant message, and (c) the extent to which outreach and victim participation succeeded in bridging the gap between the work of the ECCC and the realities of people on the ground.

Search engines play a gatekeeper role in current high-choice information environments. Considered a form of new media, users are still more likely to find and trust news found through search than social media sites. Indeed, search engines are one of the most utilised technologies to find political information, despite audits uncovering biases in their results, for example, towards national outlets over local ones. It is therefore important to keep in mind the potential of search results to affect public opinion. With this study, we investigate how Google search news headlines and snippets differ when varying migrant search terms (e.g., immigrant, refugee, expat). We employ computational text analysis methods as well as qualitative content analysis. Specifically, we employ an automated framework for detecting media frames, originally trained on Twitter data, and attempt to transfer it to news data; this framework allows for a categorization of data to frames of a generic-issue (economy, safety, health) and specific (hero:diversity, threat:jobs) nature. We evaluate its applicability for this novel data source and find that it performs well for frames related economy and security. Our next steps include analysing the results of other computational measures, namely, sentiment, agency and political outlet of the news item. We expect that sentiment and agency will complement the initial results we see based on media frames.

Computational analysis of electroencephalographic (EEG) signals have shown promising results in detecting brain disorders, such as Alzheimer’s disease (AD). AD is a progressive neurological illness that causes neuron cells degeneration, resulting in cognitive impairment. While there is no cure for AD, early diagnosis is critical to improving the quality of life of affected individuals. Here, we apply six computational time-series analysis methods (wavelet coherence, fractal dimension, quadratic entropy, wavelet energy, quantile graphs and visibility graphs) to EEG records from 160 AD patients and 24 healthy controls. Results from raw and wavelet-filtered (alpha, beta, theta and delta bands) EEG signals show that some of the time-series analysis methods tested here, such as wavelet coherence and quantile graphs, can robustly discriminate between AD patients from elderly healthy subjects. They represent a promising non-invasive and low-cost approach to the AD detection in elderly patients.

Computational enzyme design has made great strides over the last five years. Traditional methods of enzyme design require synthesis and evaluation of many mutations. Computational enzyme design has emerged as a powerful tool to predict how specific mutations modify a protein’s activity, stability, and/or selectivity. Such computational approaches can evaluate many mutations and reduce the load of in vitro work by identifying mutations likely to accomplish design objectives. Computational approaches can explore mutational spaces inaccessible in traditional mutagenesis. Computational methods reduce cost and time compared with experimental approaches. We review the efficacy and key differences of computational enzyme design methods as published in recent studies. The included articles used computational methods to design enzymes, were published no earlier than 2015, met design objectives, and verified results in vitro.