Metrics, Volume 2, Issue 2 (June 2025) – 4 articles

Many publications on early house and techno music have the character of documentation and include (auto-)biographical statements from contemporaries of the scene. This literature has led to many statements, hypotheses, and conclusions. The weaknesses of such sources are their selective and subjective nature, and the danger of unclear memories, romanticization, and constructive memory. Consequently, a validation through content-based, quantitative music analyses is desirable. For this purpose, the HOuse and Techno music from Germany and AMErica (HOTGAME) corpus was built. Metrics from the field of data quality control show that the corpus is representative and explanatory for house and techno music from Germany and the United States of America between 1984 and 1994. HOTGAME can serve as a reliable source for the analysis of early house and techno music using big data methods, like inferential statistics and machine learning. Full article

In this research, we aim to synthesize the complex issue of students’ and science teachers’ conceptions of the nature of science (NOS). We identified the conceptions of ninety-five pre-service science teachers (PSTcs) enrolled in the Qualifying Master’s Program in Teaching Science at the secondary level in Quebec (Canada) about the NOS, particularly relative to the development of science through history and approaches to constructing scientific knowledge, especially regarding the relationship between observation, hypothesis, experiment, measure and theory. To this end, we constructed a multiple-choice questionnaire (MCQ) comprising 11 statements to characterize their conceptions. The qualitative data analysis underscores the intricate nature of scientific knowledge construction. The PSTcs identified are as follows: 1. Scientific theories today correspond to improving ancient theories; 2. Science progresses by accumulation; 3. Science advancement results from improving current theories thanks to experimentation; 4. The observation is a pure observation that is preconceived; 5. We must experiment with scientific equipment in a laboratory to disprove a theory; and 6. Experiments precede scientific theory. These conceptions are crucial not only for developing training programs that help pre-service science teachers (PSTs) to study the concepts of science prescribed in the curriculum within the history and epistemology of science, but also to underscore the urgency and importance of addressing these conceptions. Full article

Scatterplots can reveal important data relationships, but their visual complexity can make pattern identification challenging. Systematic analytical approaches help structure interpretation by dividing scatterplots into meaningful regions. This paper introduces the region partitioning framework (RCF), a systematic method for dividing scatterplots into interpretable regions using k × k grids, in order to enhance visual data analysis and quantify structural changes through transformation metrics. RCF partitions the x and y dimensions into k × k grids (e.g., 4 × 4 or 16 regions), balancing granularity and readability. Each partition is labeled using an R(p, q) notation, where p and q indicate the position along each axis. Two perspectives are supported: the absolute mode, based on raw values (e.g., “very short, narrow”), and the relative mode, based on min–max normalization (e.g., “short relative to population”). I propose a set of transformation metrics—density, net flow, relative change ratio, and redistribution index—to quantify how data structures change between modes. The framework is demonstrated using both the Iris dataset and a subset of the airquality dataset, showing how RCF captures clustering behavior, reveals outlier effects, and exposes normalization-induced redistributions. Full article

In an era of evolving scholarly ecosystems, machine learning (ML) and artificial intelligence (AI) have become pivotal in advancing research impact analysis. Despite their transformative potential, the fragmented body of literature in this domain necessitates consolidation to provide a comprehensive understanding of their applications in multidimensional impact assessment. This study bridges this gap by employing bibliometric methodologies, including co-authorship analysis, citation burst detection, and advanced topic modelling using BERTopic, to analyse a curated corpus of 1608 scholarly articles. Guided by three core research questions, this study investigates how ML and AI enhance research impact evaluation, identifies dominant methodologies, and outlines future research directions. The findings underscore the transformative potential of ML and AI to augment traditional bibliometric indicators by uncovering latent patterns in collaboration networks, institutional influence, and knowledge dissemination. In particular, the scalability and semantic depth of BERTopic in thematic extraction, combined with the visualisation capabilities of tools such as CiteSpace and VOSviewer, provide novel insights into the dynamic interplay of scholarly contributions across dimensions. Theoretically, this research extends the scientometric discourse by integrating advanced computational techniques and reconfiguring established paradigms for assessing research contributions. Practically, it provides actionable insights for researchers, institutions, and policymakers, enabling enhanced strategic decision-making and visibility of impactful research. By proposing a robust, data-driven framework, this study lays the groundwork for holistic and equitable research impact evaluation, addressing its academic, societal, and economic dimensions. Full article