Escape the Metrics: The Consequences of Quantitative Approaches in Law

Business Intelligence has become in the front of demanding technologies. Many companies need technology to adapt to changing requirements, to deal with rich amounts of data; to get correct information, analysis data, and finally to quickly support decision-makers. Developing an agile BI application can be defined as a short-delivering software application. This process is based on frequently adapting to changing requirements, involving customer participation, and delivering high-quality applications. Agile teams need a set of reliable metrics to measure their performance at different three levels: functionality, content, and scalability. This paper proposes Goal Question Agility Metrics that can be used in measuring the performance of agile teams working in developing business intelligence applications. GQAM is based on the Goal Question Metrics method that was developed by Victor Basili in 1994 [19]. In addition, GQAM depends on agile and BI concepts. GQAM proposes a set of metrics that are used to measure performance. The GQAM will be subjected to experiments using a real data set in the future.

BackgroundDuring the COVID-19 pandemic, public sentiment and demands have been prominently reflected on social media platforms like Weibo. Understanding these sentiments and demands is crucial for governments, health officials, and policymakers to make effective responses and adjustments.ObjectiveThe study aims to analyze public sentiment and identify key demands concerning COVID-19 policies and social issues using Weibo data, providing insights to improve China’s policies and legal systems in public health emergencies.MethodsThe study used Python tools to collect public opinion data from Weibo regarding policy adjustments, social issues, and livelihood concerns. A total of 50,249 valid comments on 100 blog posts were collected from December 2019 to October 2023 in China. The SnowNLP algorithm was employed for sentiment analysis, Latent Dirichlet Allocation was used for topic clustering, and sampling coding was applied to further explore public demands by condensing the comment texts.ResultsThe study categorized 100 blog posts into 23 important topics, with average sentiment scores ranging from 0.24 to 0.66. These scores ranging from 0 to 1 reflect sentiment polarity, where lower values indicate more negative public sentiment. The topics of material safety and information security management had the lowest scores, at 0.24 and 0.34, respectively. The analysis further revealed that the 23 topics could be classified into 57 subtopics, and a total of 101 concepts were identified through coding. The study found that public demands fall into five key categories: transportation and travel security, epidemic protection and health security, law building and policy implementation, social services and public demand, and education demand.ConclusionsThe study underscores the complexity of public sentiment during the epidemic, with significant concerns about material safety and information security management. Public demands span basic survival needs to higher-order concerns such as education and legal protections. The findings suggest that policy-making processes must become more responsive, transparent, and equitable, incorporating real-time public feedback and ensuring comprehensive policies and legal systems are in place to address multifaceted public demands effectively.

Understanding what aspects of the urban environment are associated with better socioeconomic/liveability outcomes is a long standing research topic. Several quantitative studies have investigated such relationships. However, most of such works analysed single correlations, thus failing to obtain a more complete picture of how the urban environment can contribute to explain the observed phenomena. More recently, multivariate models have been suggested. However, they use a limited set of metrics, propose a coarse spatial unit of analysis, and assume linearity and independence among regressors. In this paper, we propose a quantitative methodology to study the relationship between a more comprehensive set of metrics of the urban environment and the valorisation of street segments that handles non-linearity and possible interactions among variables, through the use of Machine Learning (ML). The proposed methodology was tested on the French Riviera and outputs show a moderate predictive capacity (i.e., adjusted R 2 = 0.75 ) and insightful explanations on the nuanced relationships between selected features of the urban environment and street values. These findings are clearly location specific; however, the methodology is replicable and can thus inspire future research of this kind in different geographic contexts.

Investigating Interdisciplinary Practice: Methodological Challenges (Introduction)

While the concept of ‘transit insecurity’ may seem self-explanatory, the phrase is not widely used in the current lexicon of city planning, public discourse, or in policy research. The term also lacks a widely accepted definition, scale, or metric of assessment. This scoping review will provide a definition, and explore metrics that demonstrate the extent to which a person or community is transit insecure. To arrive at this definition and set of metrics, a systematic search was conducted in three electronic databases for studies pertaining to transit insecurity. Articles were screened and assessed based on inclusion/exclusion criteria. Over 200 articles were initially identified and after review, a total of 15 articles were included in the final analysis, ranging in publication from 2005 to 2020. There were four factors found to affect transit insecurities: income level; travel distance; duration; and accessibility. By examining the impact of transit insecurity we hope to shed light on these problems and assist in proposing and studying solutions to transit insecurity.

Online social networks, which have been defined as aggregated organizations that emerge from the Internet when people carry on public discussions, are increasingly becoming the vehicle of influence in social, political and economic discourse. Yet, despite its increasing importance, the nature of influence in online social networks is not really understood. Practitioners openly admit that they lack the experience to make sense of the phenomenon, and extant theory of influence in networks, which extrapolates from observations of the real world, is demonstrably inadequate when it comes to explaining influence online. The paper introduces a novel approach to analyzing influence online, which is based on the premise that knowledge flows rather than connectivity or position determine loci and regions of influence. The authors propose an exploratory, longitudinal population study of 100 highly diverse online social networks. The study will 1) benchmark a set of metrics for influence in these networks to determine which metrics are best suited for measuring influence in a plethora of contexts; 2) characterize the nature and properties of knowledge flows within each network; and 3) determine how knowledge flows impact the (virtual) spatial and temporal distribution of influence within that network.

Current and future trends in environmental, economical, and human-caused factors (such as power demand growth, over-ageing of assets in power grids, and extreme weather conditions) challenge power grid robustness in the near future, necessitating research to better analyse and understand the notion of robustness in power grids, and ultimately to enhance it. This dissertation investigates the robustness of power grids from a Complex Networks Theory perspective to develop concepts and measures to quantitatively assess power grid robustness. A set of metrics are proposed to quantitatively assess the robustness of power transmission and distribution grids accounting for the impact of the key system characteristics such as the operative state and the topology. The proposed metrics provide means to exploit the relationship between the topology, operation, and robustness performance of power grids. They are experimentally validated using models of power grids, and applied on IEEE power systems, synthetically generated power grids, and real world power grids. The proposed metrics assist grid operators for dynamical optimization of flow and topology of a given power grid, and grid analysts in strategic asset management and network expansion planning processes for the purpose of robustness enhancement of a power grid.

Research on the outcomes of Corporate Social Responsibility (CSR) on firm performance have garnered much interest in recent years, reflecting investors' growing awareness of social, environmental and corporate governance issues. The literature in this field, though vast, is littered with contradictory evidence. In addition, most studies lack a coherent set of metrics to assess CSR. Using a differentiated approach, in which firms' social responsibilities are evaluated within a multidimensional framework considering information from their annual social reports, this work aims to examine the relationship between CSR and firm performance in the Brazilian electric sector in recent years. The analysis is conducted in two basic steps: first, the Brazilian electric companies are classified according to the information disclosed from their social reports using a Data Envelopment Analysis Model. Then, several portfolios are formed based on firms' Environmental, Social and Governance (ESG) performances and are subsequently assessed using different financial metrics. The sample comprises a total of 36 electric companies and the time period of the analysis spans from 2009 to 2013.

Loss-of-control (LOC) has contributed to more fatal commercial airliner accidents and related loss of life than any other factor during the last ten years of worldwide operations. In 1997, the Commercial Aviation Safety Team (CAST) designated LOC as one of three major areas of concern in commercial aviation safety, and launched an industry-wide effort to define its causes and develop interventions to reduce its occurrence. But what constitutes “loss-of-control”? The question is not merely academic – the statistical nature of accident analysis requires that investigators group accidents by category. In some cases, the distinction between LOC and other categories, such as Controlled Flight into Terrain or Approach and Landing Operations, is unclear. In others, LOC is undoubtedly a factor, but its specific characteristics are so complex that they make analysis difficult. Consequently, investigators have called for the development of quantitative LOC measurements and criteria. In 2000, the Boeing Company and the NASA Langley Research Center jointly developed a quantitative set of metrics for defining LOC through a NASA-funded partnership under the Aviation Safety and Security Program. These metrics take the form of five envelopes relating to airplane flight dynamics, aerodynamics, structural integrity, and flight control use. These envelopes have been employed successfully in ongoing NASABoeing LOC research to indicate in a measurable sense whether LOC was a factor in an event, and to determine its onset and severity. When these criteria are used in conjunction with data from LOC events and aggressive flight test maneuvers, it becomes evident that the excursion of three or more envelopes is a reliable, quantitative indication of LOC. Furthermore, this numerical methodology can reveal new insight into the circumstances involved in LOC events, providing a valuable tool for investigators seeking viable safety interventions to reduce their occurrence.

Every activity in the web leaves a digital trace.<br>Analytics are used to measure all gathered traces along with the activities and the traffic that a user provokes, aiming to exploit the application-under-analysis with an optimal way in terms of time and cost. As a result, the question about how are they applied into learning environment, raises. This paper<br>discusses analytics dedicated to the learning process named learning analytics. After presenting their context, and a literature review regarding this relatively new term, this paper presents a methodology that is applied the academic year 2011-12 on HOU2LEARN Environment, an open educational environment set by Hellenic Open University. This methodology consists of social network analysis along with a set of metrics that is also presented. The procedure and the outcomes of this ongoing research so far are discussed.

International collaboration in research is increasingly recognized as an important component of both research and internationalization priorities by higher education institutions. This study analyzed the input---output trends of international research collaboration at five U.S. public universities using quantitative research metrics. We also tested these set of metrics to understand its individual direct relationship with international research collaboration using binary logistic regression. Results showed that international faculty, research funding, research influence, and academic impact were statistically significant (P < 0.05) and can serve as single predictors of international research collaboration for the five universities. Findings should provide international officers and research managers with clear sample data and metrics, and their association to make judgments and decisions on the value and impact of international research collaborations as they relate to overall research progress, productivity and research quality of U.S. universities.

Corruption is a complex act of crime. Its complexity does not only deal with legal issue but also social, political, and cultural issues. Law approach is more widely used in the eradication of corruption because corruption is a matter of law. However, seeing a corruption as a matter of law issue is the way to simplify the problem. The complexity of corruption makes this issue is feasibly approached through a variety of perspectives, one of those is cultural approach. The aim of this research is to reveal the cultural obstacles in eradicating corruption in Indonesia as well as to describe cultural conditions which led to a massive corruption. Formal juridical approach in eradicating corruption is considered to be done in a large number although they did not show maximal results. Such massive corruption in Indonesia seems to draw a conclusion that corruption has become a culture of the citizens of our nation. It means that there is a variety of cultural symptoms which encourage the act of corruption performed easily in every institution. There is a low mentality culture in the heart of the corruptor to carry out the action. That is why; we need a strategy in order to eradicate the culture of corruption in Indonesia.

Corruption is a complex act of crime. Its complexity does not only deal with legal issue but also social, political, and cultural issues. Law approach is more widely used in the eradication of corruption because corruption is a matter of law. However, seeing a corruption as a matter of law issue is the way to simplify the problem. The complexity of corruption makes this issue is feasibly approached through a variety of perspectives, one of those is cultural approach. The aim of this research is to reveal the cultural obstacles in eradicating corruption in Indonesia as well as to describe cultural conditions which led to a massive corruption. Formal juridical approach in eradicating corruption is considered to be done in a large number although they did not show maximal results. Such massive corruption in Indonesia seems to draw a conclusion that corruption has become a culture of the citizens of our nation. It means that there is a variety of cultural symptoms which encourage the act of corruption performed easily in every institution. There is a low mentality culture in the heart of the corruptor to carry out the action. That is why; we need a strategy in order to eradicate the culture of corruption in Indonesia.

Scientists aim to extract simplicity from observations of the complex world. An important component of this process is the exploration of data in search of trends. In practice, however, this tends to be more of an art than a science. Among all trends existing in the natural world, one-dimensional trends, often called sequences, are of particular interest, as they provide insights into simple phenomena. However, some are challenging to detect, as they may be expressed in complex manners. We present the Sequencer, an algorithm designed to generically identify the main trend in a data set. It does so by constructing graphs describing the similarities between pairs of observations, computed with a set of metrics and scales. Using the fact that continuous trends lead to more elongated graphs, the algorithm can identify which aspects of the data are relevant in establishing a global sequence. Such an approach can be used beyond the proposed algorithm and can optimize the parameters of any dimensionality reduction technique. We demonstrate the power of the Sequencer using real-world data from astronomy, geology, and images from the natural world. We show that, in a number of cases, it outperforms the popular t-Distributed Stochastic Neighbor Embedding and Uniform Manifold Approximation and Projection dimensionality reduction techniques. This approach to exploratory data analysis, which does not rely on training or tuning any parameter, has the potential to enable discoveries in a wide range of scientific domains. The source code is available on GitHub, and we provide an online interface at http://sequencer.org.

Previous works in architecture and social science found that aspects of the built environment such as density, connectivity, and house typologies are related to crime. However, these studies are qualitative, and thus hardly repeatable at larger scales. In this work, we overcome this limitation by offering a quantitative approach that explores the relationship between the configuration of the built environment and the activity of criminal groups in city areas. The method extracts a wide set of metrics related to aspects of urban form from openly accessible datasets. We then input these metrics in a step-wise logistic linear model, using presence of gang activity as dependent variable, and obtain a parsimonious model with an excellent fit when applied to the metropolitan area of London, UK. We then use values and slopes of model coefficients to build a narrative of the typical city area characterized by gang activity, re-connecting to previous theories. Outcomes of this research can help policy makers and architects in better understanding the relationship between neighborhood design and criminal activity.