Explanatory Knowledge Research Articles

„Die beste Alkoholprävention ist die Antiemanzipation“

ZusammenfassungDer Beitrag widmet sich aus einer geschlechterhistorischen Perspektive der Geschichte von sowie Diskussionen über einen konstatierten Anstieg von Frauenalkoholismus in der Bundesrepublik. Seit den 1950er Jahren beklagt der psychiatrische, pädagogische und psychologische Diskurs einen zunehmenden weiblichen Alkoholkonsum und macht die Frauenemanzipation als dessen Ursache aus. Der Artikel beleuchtet die männlich dominierten Debatten über Frauenalkoholismus bis 1968 sowie anschließend einsetzende feministische Kritiken. Er analysiert die in den Diskussionen über „die trinkende Frau“ zum Ausdruck kommenden Veränderungen der gesellschaftlichen Rolle von Frauen sowie zeitgleich sich formierende wissenschaftlich-patriarchale Gegenbewegungen. Dabei zeigt der Beitrag zum einen, wie ein klassischer Suchtbegriff aufgrund des Scheiterns medizinischer Behandlungsversuche erodiert und durch neues psychosoziales Erklärungswissen ersetzt wird. Zum anderen verdeutlicht er, wie die Frauenselbsthilfe sich dieses Wissen aneignet und neu interpretiert.

THE ADVANCEMENT OF KNOWLEDGE NOW, OR THE HISTORY OF KNOWLEDGE AND THE ELEPHANT IN THE ROOM

ABSTRACTWhat role should history play in the advancement of knowledge? Because it was so “hard,” so “unbelievably difficult … to get people to believe” in his Great Renewal, Francis Bacon thought a history of knowledge could provide evidence of advancement—a reason to “believe” and participate in his experiment. By indexing advancement, historians of knowledge could foster it. If Bacon were with us today, he would be happy to hear that the history of knowledge is a thriving enterprise. But upon reading our version, he would be dismayed to discover that advancement is nowhere to be found. It's the elephant in the room. That shouldn't surprise him or us—the great difficulty of The Great Renewal is that it always needs to be renewed—but this is a new kind of precarity. I call it friendly fire, because it's damage done not by those who wish to contain or undermine knowledge but by those whose purpose it is to produce and valorize it. Why, I ask in this article, is a volume that offers so much of value—Information: A Historical Companion—so unattuned to issues of advancement? Although occasioned by the current ubiquity of “information,” its focus is not on change—asking such questions as “why information?” and “why now?”—but on asserting the “belief” that “every age is an information age.” In a history built on that belief, change is relegated to subordinate clauses (“while recognizing changes over time”) and advancement isn't even on the table. I put it back on not by rejecting this companion but by providing a companion for it, one in which identifying and classifying change is the central task. I take two preparatory steps. First, I clarify how the concept of “culture” configures the agenda and the findings of Companion 1 while fencing out advancement. Second, I set the agenda for Companion 2 by specifying that the knowledge at stake in advancement is “explanatory knowledge.” I both address concerns about the notion of “progress” and provide a vocabulary for explanation highlighted by the concepts of “fit” and “reach.” Companion 2 then approaches the elephant from a number of angles, from a shift in information over four centuries from a matrix of currency to a matrix of possibility to the pacing of that change by a feature of the history of knowledge that I call the “sequence of surprise.” Since Bacon's highest hope for his history of knowledge was to make us better at advancing it, I conclude with a speculative turn to information's future, from Alan Turing's first use of the word “information” in its modern sense to a rethinking—through the history of knowledge—of the “hallucination” issue in our new forms of generative AI.

Understanding algorithmic recommendations. A qualitative study on children’s algorithm literacy in Switzerland

ABSTRACT Algorithmic recommendations are a central phenomenon in children’s media use, having the potential to shape their everyday lives. Therefore, the ability to consciously and critically interact with algorithmic systems – algorithm literacy – can be considered an important requirement especially for young users. How children actually understand algorithmic recommendations marks a blind spot in research. This study explores children’s algorithm literacy by conducting 26 focus groups with primary school children in Switzerland discussing video recommendations on YouTube. The qualitative analyses show that children make sense of algorithmic recommendations in quite complex ways, considering their networked, affective-involving, and economic-commercial character, indicating a general awareness, explanatory knowledge, and the beginnings of a critical stance towards recommendation engines.

"It doesn't matter if you are in charge of the trees, you always miss the trees for the forest": Power and the illusion of explanatory depth.

Power can increase overconfidence and illusory thinking. We investigated whether power is also related to the illusion of explanatory depth (IOED), people's tendency to think they understand the world in more detail, coherence, and depth than they actually do. Abstract thinking was reported as a reason for the IOED, and according to the social distance theory of power, power increases abstract thinking. We linked these literatures and tested construal style as a mediator. Further, predispositions can moderate effects of power and we considered narcissism as a candidate because narcissism leads to overconfidence and may thus increase the IOED especially in combination with high power. In three preregistered studies (total N = 607), we manipulated power or measured feelings of power. We found evidence for the IOED (regarding explanatory knowledge about devices). Power led to general overconfidence but had only a small impact on the IOED. Power and narcissism had a small interactive effect on the IOED. Meta-analytical techniques suggest that previous findings on the construal-style-IOED link show only weak evidential value. Implications refer to research on management, power, and overconfidence.

Epistemic humility and the principle of sufficient reason

ABSTRACT According to the unrestricted version of the Principle of Sufficient Reason (PSR), every truth has an explanation. I argue that there is defeasible methodological justification for belief in an unrestricted PSR. The argument is based on considerations about our cognitive limitations. It is possible that our cognitive limitations prevent us from even recognizing the explanatorily open character of some propositions we can now represent: the fact that these propositions are explicable in the first place. If this is the case, then a proper recognition of our limitations and a standing goal of reasoning to attain complete explanatory knowledge justify the adoption of an unrestricted PSR.

Improving decision making through visual knowledge calibration

PurposeThis article aims to explore the so-called illusion of explanatory depth (IOED) of managers regarding their understanding of digital technologies and examines the effect of knowledge visualization one’s current understanding and decision making. Its purpose is to show that managers think they know more than they do and that this affects decision making but can be reduced through knowledge visualization.Design/methodology/approachIn two experiments with experienced managers, the authors investigate the size and impact of the IOED bias in decision making and examine if sketched self-explanations are as effective as written self-explanations to reduce the bias.FindingsThe findings show that experienced managers suffer from a significant illusion concerning their explanatory understanding of digital technologies and that sketching one’s current level of explanatory understanding of these technologies supports the accurate calibration of one’s knowledge. The findings indicate that sketching knowledge is a helpful modality for the detection and subsequent recalibration of biased knowledge in domain-dependent decision making.Originality/valueThis article is the first to explore the effect of sketched knowledge externalization on the calibration of explanatory knowledge of managers. It extends the literature on both, the IOED and on knowledge visualization as an instrument of knowledge calibration.

An automatic visible explainer of geometric knowledge for aeroshape design optimization based on SHAP

Aerodynamic shape design may involve complicated geometric optimization problems. When the number of geometric decision variables increases, the optimization algorithms can still search for solutions automatically, but it would work more like a black-box, for which the explanation will be of great help. This paper focuses on further mining the explanatory information of the shape optimization. A multi-objective optimization of a spacecraft is performed as a simplified case study. The LightGBM meta-model is built for the response from the geometric variables to the objectives, followed the SHAP explainer to quantify the contributions of variables. To visually interpret the deformation on shape, we propose the SHAP-Shape Map method to translate the attribution from the deformation features to the shape and make the attribution visible on the mesh surface. Much explanatory knowledge is easily obtained, for example, without seeing any flow field or pressure distribution image, the explainer successfully inferred that the significant influence of the drag coefficient occurs at the tip of the shape's nose. Furthermore, it has almost no barrier to learn that the trade-offs (conflicts) between the objectives are mainly concentrated in the nose as well. More importantly, with the proposed method, the above analysis results can be reached by anyone without specialized expertise of aerodynamics or geometric parameterization.

The evolution of the Fintech entrepreneurial ecosystem in Africa: An exploratory study and model for future development

Despite Africa's leadership in mobile money innovation and an increasing level of Fintech entrepreneurial activity, Africa's Fintech ecosystem trajectory is under-represented in the literature. This study is set out to redress this anomaly while addressing the tensions on the role of history, and adaptability in entrepreneurial ecosystem research. We draw upon the Fintech and the entrepreneurial ecosystem literature, analyze the views of practitioners, and apply temporal bracketing to reconstruct the historical context and three phases of the African Fintech ecosystem evolution over a decade: mobile money, digital finance, and innovative Fintech. Based on the results, we develop a model that explains the metamorphosis of entrepreneurial ecosystems. The study contributes an African narrative to the descriptive and explanatory knowledge of Fintech. By showing how Africa's Fintech entrepreneurial ecosystem has evolved, the study also contributes to the entrepreneurial ecosystems research that has thus far focused more on place, actors, and governance than evolution. Our model adds a dynamic perspective to the theorization of Fintech ecosystems and extends the existing literature by taking a historical and longitudinal foresight. Practitioners can apply the lessons to enhance polices that support future Fintech entrepreneurship.

Practice Frameworks in the Correctional Domain: How Values Drive Knowledge Generation and Treatment.

Practice frameworks are a unique type of theory that bridge the gap between abstract explanatory theories, knowledge categories, normative assumptions, and treatment theories. The values component of practice frameworks is particularly critical as it frames the problem space (niches) within which practitioners engage in practice tasks, for example, moral repair, risk reduction, or community integration. It also picks out the specific kinds of entities and their relationships that researchers are most interested in. In effect, values and their related principles play a major role in abstractly mapping out domains of practice while the knowledge related assumptions (e.g., core categories and causal powers) flesh out core entities and processes indicated by these values. In this paper we provide an overview of practice frameworks and examine the role of their normative assumptions in mapping out correctional research and practice tasks.

The Effects of Digital Technology on Opportunity Recognition

Recognizing opportunities enabled by digital technology (DT) has become a competitive necessity in today’s digital world. However, opportunity recognition is a major challenge given the influence of DT, which not only disperses agency across various actors, but also blurs boundaries between customers, companies, products, and industries. As a result, traditional entrepreneurship knowledge needs to be rethought and the effects of DT on opportunity recognition need to be better understood. Drawing from opportunity recognition theory – as one of the central theories in the entrepreneurship domain – this study builds on a structured literature review to identify and explain three direct as well as three transitive effects of DT on opportunity recognition. These effects have been validated with real-world cases as well as interviews with academics and practitioners. In sum, this study contributes to descriptive and explanatory knowledge on the evolution from traditional to digital entrepreneurship. As a theory for explaining, the findings extend opportunity recognition theory by illuminating how and why DT influences opportunity recognition. This supports research and practice in investigating and managing opportunities more effectively.

Predicting Yarn Breaks in Textile Fabrics: A Machine Learning Approach

In this paper, we propose a Machine Learning (ML) approach to predict faults that may occur during the production of fabrics and that often cause production downtime delays. We worked with a textile company that produces fabrics under the Industry 4.0 concept. In particular, we deal with a client customization requisite that impacts on production planning and scheduling, where there is a crucial need of limiting machine stoppage. Thus, the prediction of machine stops enables the manufacturer to react to such situation. If a specific loom is expected to have more breaks, several measures can be taken: slower loom speed, special attention by the operator, change in the used yarn, stronger sizing recipe, etc. The goal is to model three regression tasks related with the number of weft breaks, warp breaks, and yarn bursts. To reduce the modeling effort, we adopt several Automated Machine Learning (AutoML) tools (H2O, AutoGluon, AutoKeras), allowing us to compare distinct ML approaches: using a single (one model per task) and Multi-Target Regression (MTR); and using the direct output target or a logarithm transformed one. Several experiments were held by considering Internet of Things (IoT) historical data from a Portuguese textile company. Overall, the best results for the three tasks were obtained by the single-target approach with the H2O tool using logarithm transformed data, achieving an R2 of 0.73 for weft breaks. Furthermore, a Sensitivity Analysis eXplainable Artificial Intelligence (SA XAI) approach was executed over the selected H2OAutoML model, showing its potential value to extract useful explanatory knowledge for the analyzed textile domain.

Problem-Based Learning Method and the Learning Outcomes in Science 9

This study aimed to determine the learning outcomes of the students in Science 9 through the use of different problem-based learning methods. Furthermore, this attempted to determine the student performance outcome as to their problem-based learning in terms of explanatory knowledge, descriptive knowledge, procedural knowledge, personal knowledge. Likewise, this study aimed to find out if there was a significant difference in the students' problem-based learning outcomes when grouped according to the teaching method as to explanation problem, fact-finding problem, strategy problem, and moral dilemma. Using an experimental design of research, it involved four groups in Grade 9 at San Antonio National High School with 40 students each during the Academic Year 2020-2021. A teacher-made module and performance task rubrics were used to measure the learning outcomes of the students when group according to the problem-based learning method which was validated by the panel of experts. The study used descriptive statistics such as frequency, percentage, mean and standard deviation to interpret the scores in the performance outcome of the respondents. Analysis of Variance (ANOVA) was also used to determine the possible presence of a significant difference in the students' problem-based learning outcomes when group according to the teaching method. Result revealed that there is a significant difference in the students' problem-based learning outcomes when group according to the teaching method as to explanation problem, fact-finding problem, strategy problem and moral dilemma. It also showed that students performance outcome as to their PBL in terms of explanatory knowledge, descriptive knowledge, procedural knowledge and personal knowledge fall under the category of proficient level.

Measuring the impact of productive failure on nursing students' learning in healthcare simulation: A quasi-experimental study

BackgroundPrevious research suggests that making errors in a non-threatening simulated environment can facilitate learning. Productive failure, which combines problem-solving tasks followed by instruction, enables students to learn from making mistakes. This teaching approach has demonstrated improved learning outcomes such as explanatory knowledge and transfer of knowledge compared to a direct instruction approach where students receive instruction prior to problem-solving tasks. However, no previous studies have examined the impact of productive failure on nursing students' learning in manikin-based simulation. ObjectiveTo measure the impact of productive failure on nursing students' declarative knowledge, explanatory knowledge, and transfer of knowledge compared to a direct instruction approach in a paediatric closed head injury simulation. MethodsSecond year undergraduate nursing students (n = 349) from one Australian university were invited to participate in the study. Consenting participants (n = 344) were randomised into two groups: productive failure and direct instruction. The intervention consisted of two paediatric closed head injury simulations separated by a simulation debrief. Knowledge tests were administered before and immediately after the simulation. ResultsData from 331 participants were analysed. The productive failure group outperformed the direct instruction group in the post-test (p < 0.001). Learning gains for participants in the productive failure group were significantly higher than the direct instruction group for both explanatory knowledge (p < 0.001) and the ability to apply learning to solve novel clinical problems (p < 0.001). The difference in the median scores for declarative knowledge was not significant (p = 0.096). ConclusionThis study demonstrated that a productive failure simulation that leads learners to make mistakes before receiving instruction can facilitate deeper levels of explanatory knowledge and enable the transfer of learning to new clinical situations. These results suggest the need for further exploration of pedagogies that foster learning from errors in simulation-based learning.

Translingual Literary Text: on Problem of Understanding

The given study covers an actual interdisciplinary issue - Russian language, post-Soviet Russian literature in particular, that includes the otherness of multiple ethnic cultures and creates unique images of the world. In the modern conventional sense, culture is replaced by transculture - a space of interaction and mutual repulsion, intertwinement, constellation, overlapping, flowing of cultures into one another. These processes have no and cant have any solidified, final forms that would be determined once and for all. Therefore, the works created in the aesthetics of transculturation are always unique, be it a literary text, a musical message or a silent arthouse short film speaking the language of negative space. We believe that a transcultural episteme should be used in the process of new thinking formation. A person without any developed pragmatic presupposition is deprived of explanatory knowledge and becomes a victim of the information manipulation embedding a model of confrontational perception of the Other into the collective consciousness. By the given work, we would like to demonstrate a method of working with higher-school students that we called Immersion Reading. Using the works by Russian Germans (in particular, E. Seifert and G. Belger), we describe the stages of readers immersion into a literary work step by step (context verticalization, hermeneutic comment creation, stages of typification and differentiation of texts within a chosen paradigm, synthesis) and then bring up the results of our work with students and doctoral candidates of Russia and Kazakhstan for discussion within Literature and Globalization, Intercultural Communication in Art Dimension lecture courses (author of the courses - Bakhtikireeva, U.M.).

Creating ‘Deep Knowledge’ and Transformative Change: A Critical Social Work Approach to Researching Formal Kinship Care

Abstract In a context of rapidly changing social and economic conditions and increasing practice complexity, critical research perspectives can create in-depth explanatory knowledge for social work practice. Drawing on a broader knowledge base, these approaches provide a comprehensive view of social phenomena and the causes of personal and social harm. They also offer a framework to guide ethical intervention based on principles of collaboration, social justice and social transformation. The aim of this article is to demonstrate how critically oriented research can deliver useful and actionable knowledge directly to the field and promote transformative change. The exemplar presented here is a critical participatory study exploring formal kinship care in Victoria, Australia. The research partnered actively with service users and practitioners, with mixed methods utilised and data analysed for content and themes. Findings revealed dissonant assumptions in relation to roles and responsibilities as an underlying cause of care and practice issues. New understandings and practical solutions for the field were generated by exploring study findings with stakeholders. This application of a critical research approach to the field of formal kinship care establishes that the method can provide a coherent, effective and ethical framework for inquiry.

Colonialism, science, and health

In addition to genocide, slavery, and the dispossession of indigenous people, colonialism, as a form of control, meant the suppression of traditional knowledge. The imposition of Christianity, the modern Western paradigm, and modern science that followed perpetrated this suppression. The universal role held by modern science is supported neither by epistemic nor social aspects. It is ineffective and complicit in the collapse of civilization, and it is worsened by comprehensive and unifying ideas to be reduced to an input-process of technological innovation for the benefit of social control industries such as the military, information technology, communication, or health. Furthermore, it suppresses ancestral knowledge related to health and medicine that may be beneficial and must be researched (stimulant medicines). Coupled with the health industry, it promotes the medicalization of life, spreading uncertainty, anxiety, and unease. Therefore, it is an instrument of neocolonialism that imposes its priorities, supplanting problems in subordinated countries, and extracts substantial resources, which is detrimental to social policies and programs. The biggest objection to the universality of modern science is derived from its empiricist and reductionist nature. Through the practically impossible idea of a unifying and explanatory knowledge, it impedes researchers the understanding of the complexity of the world and their historical moment and to act accordingly. It transforms great creative and liberating potential to submissiveness for the interests of capital and its representatives.

The sociological determinants of scientific bias

ABSTRACT Science is an ethical community whose practitioners aim to discover information about the natural world and to explain discernible patterns that might be detected. Those who pursue science generally embrace certain epistemic values that help establish the moral boundaries of the community, while the twin pillars of rationality and empiricism serve as the foundations upon which scientists establish their truth claims. Yet however robust the assertions might appear, they nevertheless are the by-products of an exclusively human endeavor directly impacted by those sociological forces that apply throughout the social universe, including the scientist’s social location and the importance of enhancing one’s reputation. The current paper identifies key sociological factors that help shape scientific bias and the nature of the justifications used to defend truth claims. A case study of one community committed to a sociological paradigm demonstrates the utility of the explanatory framework advanced. A more self-conscious awareness of the forces at play that create such biases can help mitigate their deleterious impacts that subvert the quest for explanatory knowledge and valid truth claims about observable phenomena.

Meanings of Theory: Clarifying Theory through Typification

AbstractDeveloping and evaluating scientific knowledge and its value requires a clear – or at least not too unclear – understanding of what ‘theory’ means. We argue that common definitions of theory are too restrictive, as they do not acknowledge the existence of multiple kinds of scientific knowledge, but largely recognize only one kind as ‘theory’, namely explanatory knowledge. We elaborate a typology that broadens and clarifies the meaning of ‘theory’. Consisting of five basic theory types – explaining, comprehending, ordering, enacting and provoking theories – the typology offers a framework that enables researchers to develop and assess knowledge in more varied ways and for a broader set of purposes than is typically recognized, as well as providing a more level playing field within the academic community.

Explanatory Q&A recommendation algorithm in community question answering

PurposeIn community question answering (CQA), people who answer questions assume readers have mastered the content in the answers. Nevertheless, some readers cannot understand all content. Thus, there is a need for further explanation of the concepts that appear in the answers. Moreover, the large number of question and answer (Q&A) documents make manual retrieval difficult. This paper aims to alleviate these issues for CQA websites.Design/methodology/approachIn the paper, an algorithm for recommending explanatory Q&A documents is proposed. Q&A documents are modeled with the biterm topic model (BTM) (Yan et al., 2013). Then, the growing neural gas (GNG) algorithm (Fritzke, 1995) is used to cluster Q&A documents. To train multiple classifiers, three features are extracted from the Q&A categories. Thereafter, an ensemble classification model is constructed to identify the explanatory relationships. Finally, the explanatory Q&A documents are recommended.FindingsThe GNG algorithm shows good clustering performance. The ensemble classification model performs better than other classifiers. The both effect and quality scores of explanatory Q&A recommendations are high. These scores indicate the practicality and good performance of the proposed recommendation algorithm.Research limitations/implicationsThe proposed algorithm alleviates information overload in CQA from the new perspective of recommending explanatory knowledge. It provides new insight into research on recommendations in CQA. Moreover, in practice, CQA websites can use it to help retrieve Q&A documents and facilitate understanding of their contents. However, the algorithm is for the general recommendation of Q&A documents which does not consider individual personalized characteristics. In future work, personalized recommendations will be evaluated.Originality/valueA novel explanatory Q&A recommendation algorithm is proposed for CQA to alleviate the burden of manual retrieval and Q&A overload. The novel GNG clustering algorithm and ensemble classification model provide a more accurate way to identify explanatory Q&A documents. The method of ranking the explanatory Q&A documents improves the effectiveness and quality of the recommendation. The proposed algorithm improves the accuracy and efficiency of retrieving explanatory Q&A documents. It assists users in grasping answers easily.

Concepções alternativas sobre tectônica de placas

Este artigo apresenta uma avaliação do entendimento dos alunos sobre Tectônica de Placas em um curso técnico de Mineração. Por intermédio de um questionário, aplicaram-se perguntas sobre terminologia, movimento relativo de placas, fusão de rochas e ocorrência de terremotos. O objetivo da abordagem foi permitir que os participantes expressassem mal-entendidos, entendimentos parciais e outras concepções alternativas, de modo a auxiliar a aprendizagem posterior. Foi observado que os alunos apresentam algum conhecimento descritivo das placas tectônicas, mas é inconsistente o conhecimento explicativo conceitual, que relaciona a dinâmica dos movimentos das placas com as evidências explicadas pela teoria. Constatou-se a necessidade do uso de evidências e conexões dinâmicas entre movimentos de placas e processos observáveis para apoiar o aprendizado da teoria. Ainda, destacou-se a importância de se realizar uma aula focada em refutar concepções alternativas dos alunos sobre a Teoria de Tectônica de Placas.