An optimized back propagation neural network on small samples spectral data to predict nitrite in water.

Accurate detection of pollutant levels in water bodies using fusion algorithms combined with spectral data has become a critical issue for water conservation. However, the number of samples is too small and the model is unstable, which often leads to poor prediction and fails to achieve the measurement goal well. To address these challenges, this paper proposes a practical and effective method to precisely predict the concentrations of nitrite pollution in aquatic environments. The proposed method consists of three steps. Firstly, the dimension of the spectral data is reduced using Kernel Principal Component Analysis (KPCA), followed by sample augmentation using Generative Adversarial Network (GAN) to reduce calculation cost and increase the diversity and scale of the data. Secondly, several improvement strategies, including multi-cluster competitive and adaptive parameter updating, are introduced to enhance the capability of the Particle Swarm Optimization (PSO) algorithm. The improved PSO algorithm is then applied to optimize the initialization weights and biases of the Back Propagation neural network, thereby improving the model fitting and training performance. Finally, the developed prediction model is employed to predict the test set samples. The result suggests that the R2, RMSE, and MAE values are 0.976290, 0.008626, and 0.006617, which outperform the state-of-the-art and provided a promising model for the prediction of nitrite concentration in water.

What counts as relevant criticism? Longino's critical contextual empiricism and the feminist criticism of mainstream economics.

I identify and resolve an internal tension in Critical Contextual Empiricism (CCE) - the normative account of science developed by Helen Longino. CCE includes two seemingly conflicting principles: on one hand, the cognitive goals of epistemic communities should be open to critical discussion (the openness of goals to criticism principle, OGC); on the other hand, criticism must be aligned with the cognitive goals of that community to count as "relevant" and thus require a response (the goal-relativity of response-requiring criticism principle, GRC). The co-existence of OGC and GRC enables one to draw both approving and condemning judgments about a situation in which an epistemic community ignores criticism against its goals. This tension results from conflating two contexts of argumentation that require different regulative standards. In the first-level scientific discussion, GRC is a reasonable principle but OGC is not; in the meta-level discussion about science, the reverse holds. In meta-level discussion, the relevance of criticism can be established by appealing to goals of science that are more general than the goals of a specific epistemic community. To illustrate my revision of CCE, I discuss why feminist economists' criticism of the narrowness of the goals pursued in mainstream economics is relevant criticism.

Norm conflicts and epistemic modals.

Statements containing epistemic modals (e.g., "by spring 2023 most European countries may have the Covid-19 pandemic under control") are common expressions of epistemic uncertainty. In this paper, previous published findings (Knobe & Yalcin, 2014; Khoo & Phillips, 2018) on the opposition between Contextualism and Relativism for epistemic modals are re-examined. It is found that these findings contain a substantial degree of individual variation. To investigate whether participants differ in their interpretations of epistemic modals, an experiment with multiple phases and sessions is conducted to classify participants according to the three semantic theories of Relativism, Contextualism, and Objectivism. Through this study, some of the first empirical evidence for the kind of truth-value shifts postulated by semantic Relativism is presented. It is furthermore found that participants' disagreement judgments match their truth evaluations and that participants are capable of distinguishing between truth and justification. In a second experimental session, it is investigated whether participants thus classified follow the norm of retraction which Relativism uses to account for argumentation with epistemic modals. Here the results are less favorable for Relativism. In a second experiment, these results are replicated and the normative beliefs of participants concerning the norm of retraction are investigated following work on measuring norms by Bicchieri (2017). Again, it is found that on average participants show no strong preferences concerning the norm of retraction for epistemic modals. Yet, it was found that participants who had committed to Objectivism and had training in logic applied the norm of retraction to might-statements. These results present a substantial challenge to the account of argumentation with epistemic modals presented in MacFarlane (2014), as discussed.

Argument and explanation.

In this paper, we bring together two closely related, but distinct, notions: argument and explanation. We clarify their relationship. We then provide an integrative review of relevant research on these notions, drawn both from the cognitive science and the artificial intelligence (AI) literatures. We then use this material to identify key directions for future research, indicating areas where bringing together cognitive science and AI perspectives would be mutually beneficial. This article is part of a discussion meeting issue 'Cognitive artificial intelligence'.

A method for assessing students' interpretations of contextualized data.

Learning to interpret data in context is an important educational outcome. To assess students' attainment of this outcome, it is necessary to examine the interplay between their contextual and statistical reasoning. We describe a research method designed to do so. The method draws upon Toulmin's (1958, 2003) model of argumentation for the first stage of qualitative data analysis and the Structure of the Observed Learning Outcome (SOLO) (Biggs & Collis, 1991) model for the second stage. Toulmin analyses help identify the justifications and expressions of uncertainty students provide in their interpretive arguments. Subsequent analyses based on the multi-modal conceptualization of SOLO help characterize the quality of student arguments relative to one another. Existing literature and an empirical example are drawn upon to explain how the Toulmin and SOLO models can be used in tandem to analyze students' interpretations of contextualized data. We also explain how pairing Toulmin and SOLO can address theoretical and practical limitations that arise when using just one of the two models on its own.

The Impact of Collaboration Between Science and Religious Education Teachers on Their Understanding and Views of Argumentation.

Teachers' understanding and teaching of argumentation is gaining more attention in science education research. However, little is known about how science teachers engage in argumentation with teachers of different subject taking an interdisciplinary perspective that may inspire new pedagogical ideas or strategies. In particular, the positioning of argumentation at the juncture of science and religion is rare. This paper reports an empirical study involving science and religious education (RE) teachers who collaborated on teaching argumentation in three secondary schools in England. Their interdisciplinary collaboration was sustained by a series of professional development sessions over 18 months. Analysis of the interview data unfolds how the teachers' collaboration impacted their understanding of argumentation and views of teaching their subject. Through working relationally in exploring and teaching argumentation, the science teachers reflected more notable changes than their RE counterparts. Science teachers came to appreciate student voice in the learning process and the role of argumentation in fostering students' scientific reasoning. The paper is a salient step to researching argumentation in a cross-curricular terrain, particularly in relation to RE. It also sheds light on how collaborating with teachers of another subject bolstered science teachers' professional development and broke subject barriers.

Design, implementation, and evaluation of an online supported peer feedback module to enhance students' argumentative essay quality.

We know little to what extent peer feedback strategies can be applied on a large scale in higher education for complex tasks. This study aimed to design, implement, and evaluate an online-supported peer feedback module for large-scale use to enhance higher education students' argumentative essay writing performance. To do this, 330 students from five different courses at bachelor and master levels followed the online supported peer feedback module. In this module, students were asked to write an argumentative essay about a controversial issue, provide peer feedback for two peers, and revise their original essays based on the received feedback. Three types of data including original essay (pre-test) data, peer feedback data, and revised essay (post-test) data collected. Students also filled out the learning satisfaction questionnaire at the end of the module. The findings showed that the suggested online-supported peer feedback module was effective in improving students' argumentative essay quality in all courses at the bachelor and master levels. The findings also showed there is a difference in the level of students' satisfaction with the module among the courses and between the education levels. The findings of this study provide insights into and add value to the scalability of online peer feedback tools for argumentative essay writing in different contexts. Based on the findings, recommendations for future studies and educational practice are provided.

Can exposure to arguments pro and contra extraversion affect self-reports of the trait and the attitude toward it?

OBJECTIVE: Changeability of personality over short-term intervals has increasingly become a focus of research. However, the role played by argumentation interventions in short-term variations has scarcely been examined. METHODS: In two experiments (N = 363 and 320), we investigated how processing positive and negative argumentation regarding extraversion (Study 1: watching a lecture; Study 2: elaborating self-invented arguments) affects self-reports on this trait and attitude toward it. The experiments included three waves of measurements with argument manipulation (in favor of or against extraversion) immediately prior to Time 2 (Study 2 also included a control group). RESULTS: Mean-level changes in extraversion across time moments, measured with the longitudinal confirmatory factor analysis, were consistently negligible. Conversely, there were some indications that argumentation about extraversion could have immediate short-term effects on attitudes toward this trait. The random-intercept cross-lagged model showed that rank-order consistency stemmed from a trait-like intercept, which was particularly large for trait extraversion compared with the attitude. The autoregressive and cross-lagged effects of residual within-person variation were consistently small and mostly nonsignificant. CONCLUSION: Our findings suggest that extraversion and the attitude toward it maintained their temporal continuity within 3 months, even under a single exposure to arguments pro and contra this trait.

Argumentation Analysis of Risk Assessments: The Case of Perfluorooctanoic Acid.

Risk assessment of chemicals can be based on toxicology and/or epidemiology. The choice of toxicological or epidemiological data can result in different health-based guidance values (HBGVs). Communicating the underlying argumentation is important to explain these differences to the public and policymakers. In this article, we explore the argumentation used to justify the use of toxicological or epidemiological data in the derivation of HBGVs in four different risk assessments for the chemical Perfluorooctanoic acid (PFOA). The pragma-dialectical argumentation theory (PDAT) is hereby applied. The argumentations to select relevant health endpoints or certain studies to infer causality appeared mainly based on "symptomatic relations," that is, study results are used as characteristic of what was claimed to be a causal relation without delving into the actual causal argumentation that preceded it. Starting points that are at the basis of the chain of arguments remained implicit. Argumentation to use epidemiological and/or toxicological data was only briefly mentioned and the underlying argumentative foundation that led to the conclusion was seldom found or not addressed at all. The decision to include/exclude information was made based on the availability of data, or the motives for the choice remained largely unclear. We conclude that more depth in argumentation and a subordinative chain of arguments is needed to better disclose the underlying reasoning leading to a certain health-based guidance value (HBGV). More explicit identification and discussion of starting points could be a valuable addition to general risk assessment frameworks for maximum use of toxicological and epidemiological data and shared conclusions of the assessment.

Online selection of a physician by patients: the impression formation perspective.

BACKGROUND: With the rapid development of online health communities (OHCs), an increasing number of physicians provide services in OHCs that enable patients to consult online in China. However, it is difficult for patients to figure out the professional level of doctors before consultation and diagnosis because of information asymmetry. A wealth of information about physicians is displayed in their profiles as a new way to help patients evaluate and select quickly and accurately. OBJECTIVE: This research explores how the profile information (PI) presented in OHCs influences patients' impression formation, especially the perception of professional capital (i.e., status capital and decisional capital). The impression influences their intention to consult further, which is partially mediated by the initial trust. The Toulmin's model of argumentation is used to decide the strength of the argument presented in physicians' homepage information and divide it into claim, data, and backing. METHODS: This study conducts an internet experiment and recruits 386 subjects through the internet to investigate the effect of impression formation on online selection behavior by a patient. RESULTS: The results show that the strength of argument has a significant positive association with the perception of professional capital. Perceptions of professional capital are highest when a fully composed argument (claim/data/backing) is included in a profile, with claim/data being the next highest and claim only the lowest. Recommendations from connections have the strongest impact. In turn, patients' selection decisions are influenced by their perception of professional capital, which is partially mediated by initial trust. CONCLUSIONS: This study is significant in terms of its implications for theory and practice. On the one hand, this research contributes to the online health community literature and suggests that the perception of professional capital on physicians should be pre-presumed and built based on the information before in-person interaction online. On the other hand, this study is helpful in understanding the effect of various components included in PI on perceiving physicians' abilities, and not all information is equally important.

Learning analytics dashboards for adaptive support in face-to-face collaborative argumentation.

Despite the potential of learning analytics for personalized learning, it is seldom used to support collaborative learning particularly in face-to-face (F2F) learning contexts. This study uses learning analytics to develop a dashboard system that provides adaptive support for F2F collaborative argumentation (FCA). This study developed two dashboards for students and instructors, which enabled students to monitor their FCA process through adaptive feedback and helped the instructor provide adaptive support at the right time. The effectiveness of the dashboards was examined in a university class with 88 students (56 females, 32 males) for 4 weeks. The dashboards significantly improved the FCA process and outcomes, encouraging students to actively participate in FCA and create high-quality arguments. Students had a positive attitude toward the dashboard and perceived it as useful and easy to use. These findings indicate the usefulness of learning analytics dashboards in improving collaborative learning through adaptive feedback and support. Suggestions are provided on how to design dashboards for adaptive support in F2F learning contexts using learning analytics.

A review of technologies for collaborative online information seeking: On the contribution of collaborative argumentation.

In everyday life, people seek, evaluate, and use online sources to underpin opinions and make decisions. While education must promote the skills people need to critically question the sourcing of online information, it is important, more generally, to understand how to successfully promote the acquisition of any skills related to seeking online information. This review outlines technologies that aim to support users when they collaboratively seek online information. Upon integrating psychological-pedagogical approaches on trust in and the sourcing of online information, argumentation, and computer-supported collaborative learning, we reviewed the literature (N = 95 journal articles) on technologies for collaborative online information seeking. The technologies we identified either addressed collaborative online information seeking as an exclusive process for searching for online information or, alternatively, addressed online information seeking within the context of a more complex learning process. Our review was driven by three main research questions: We aimed to understand whether and how the studies considered 1) the role of trust and critical questioning in the sourcing of online information, 2) the learning processes at play when information seekers engage in collaborative argumentation, and 3) what affordances are offered by technologies that support users' collaborative seeking of online information. The reviewed articles that focused exclusively on technologies for seeking online information primarily addressed aspects of cooperation (e.g., task management), whereas articles that focused on technologies for integrating the processes of information seeking into the entire learning processes instead highlighted aspects of collaborative argumentation (e.g., exchange of multiple perspectives and critical questioning in argumentation). Seven of the articles referred to trust as an aspect of seekers' sourcing strategies. We emphasize how researchers', users', and technology developers' consideration of collaborative argumentation could expand the benefits of technological support for seeking online information.

How to Play the "Playing God" Card.

When the phrase "playing God" is used in debates concerning the use of new technologies, such as cloning or genetic engineering, it is usually interpreted as a warning not to interfere with God's creation or nature. I think that this interpretation of "playing God" arguments as a call to non-interference with nature is too narrow. In this paper, I propose an alternative interpretation of "playing God" arguments. Taking an argumentation theory approach, I provide an argumentation scheme and accompanying critical questions that capture the moral concerns expressed by "playing God" arguments. If I am right, then "playing God" arguments should be understood, not as a warning to leave God's creation or nature alone, but rather as an invitation to think carefully about all the ways in which the use of new technologies could go seriously wrong.

Using Historical Scientific Controversies to Promote Undergraduates' Argumentation.

The Covid-19 pandemic is the reason why humanity is paying more attention to the importance of regular and rigorous handwashing. Interestingly, in the nineteenth century, regular and rigorous handwashing was a key (and controversial) solution proposed by the Hungarian obstetrician Ignaz Philipp Semmelweis to cut drastically cases of puerperal fever. The purpose of this study was to provide evidence that the case of Semmelweis and puerperal fever-a crucial historical scientific controversy-can be used as a springboard to promote university student argumentation. Our study was inspired by the fact that the Organization for Economic and Cooperative Development (OECD) stressed that more efforts and resources should be invested in promoting argumentation as an essential component for scientifically literate citizens in twenty-first century societies. However, nowadays, argument and debate are virtually absent from university science education. The data was derived from 124 undergraduates' (64 females and 60 males, 15-30 years old) written responses and audio and video recordings in a university biology course in Colombia. The findings show that the articulation of this historical controversy with decision-making, small-group debate, and whole-class debate activities can be useful for promoting undergraduates' argumentation. This study contributes to the development of a research-based university science education that can inform the design of an argumentation curriculum for higher education.

Reflecting on shared decision making: A reflection-quantification study.

BACKGROUND: Reflecting ("stop-and-think") before rating may help patients consider the quality of shared decision making (SDM) and mitigate ceiling/halo effects that limit the performance of self-reported SDM measures. METHODS: We asked a diverse patient sample from the United States to reflect on their care before completing the 3-item CollaboRATE SDM measure. Study 1 focused on rephrasing CollaboRATE items to promote reflection before each item. Study 2 used 5 open-ended questions (about what went well and what could be improved upon, signs that the clinician understood the patient's situation, how the situation will be addressed, and why this treatment plan makes sense) to invite reflection before using the whole scale. A linear analogue scale assessed the extent to which the plan of care made sense to the patient. RESULTS: In Study 1, 107 participants completed surveys (84% response rate), 43 (40%) rated a clinical decision of which 27 (63%) after responding to reflection questions. Adding reflection lowered CollaboRATE scores ("less" SDM) and reduced the proportion of patients giving maximum (ceiling) scores (not statistically significant). In Study 2, 103 of 212 responders (49%) fully completed the version containing reflection questions. Reflection did not significantly change the distribution of CollaboRATE scores or of top scores. Participants indicated high scores on the sense of their care plan (mean 9.7 out of 10, SD 0.79). This rating was weakly correlated with total CollaboRATE scores (rho = .4, P = .0001). CONCLUSION: Reflection-before-quantification interventions may not improve the performance of patient-reported measures of SDM with substantial ceiling/halo effects.

A Bayesian Argumentation Framework for Distributed Fault Diagnosis in Telecommunication Networks.

Traditionally, fault diagnosis in telecommunication network management is carried out by humans who use software support systems. The phenomenal growth in telecommunication networks has nonetheless triggered the interest in more autonomous approaches, capable of coping with emergent challenges such as the need to diagnose faults' root causes under uncertainty in geographically-distributed environments, with restrictions on data privacy. In this paper, we present a framework for distributed fault diagnosis under uncertainty based on an argumentative framework for multi-agent systems. In our approach, agents collaborate to reach conclusions by arguing in unpredictable scenarios. The observations collected from the network are used to infer possible fault root causes using Bayesian networks as causal models for the diagnosis process. Hypotheses about those fault root causes are discussed by agents in an argumentative dialogue to achieve a reliable conclusion. During that dialogue, agents handle the uncertainty of the diagnosis process, taking care of keeping data privacy among them. The proposed approach is compared against existing alternatives using benchmark multi-domain datasets. Moreover, we include data collected from a previous fault diagnosis system running in a telecommunication network for one and a half years. Results show that the proposed approach is suitable for the motivational scenario.

Rebuttal: Doing Phenomenology on the Things.

This rebuttal responds to the article "Getting it quite wrong" (published in this journal issue of QHR). My work is described as "amassing experiential descriptions," simply aiming to "reproduce the original experience unaltered," naively claiming "that the fundamental question of phenomenology is to understand what it is like to have this or that experience" and other such injudicious points. I take issue with these claims. Husserl is quoted as supportively stating that "phenomenology was from the beginning never supposed to be anything except the path to a radically genuine 'strictly scientific metaphysics.'" I will show with textual examples that the presented view of phenomenology is too limited and one sided.

Modelling competing legal arguments using Bayesian model comparison and averaging.

Bayesian models of legal arguments generally aim to produce a single integrated model, combining each of the legal arguments under consideration. This combined approach implicitly assumes that variables and their relationships can be represented without any contradiction or misalignment, and in a way that makes sense with respect to the competing argument narratives. This paper describes a novel approach to compare and 'average' Bayesian models of legal arguments that have been built independently and with no attempt to make them consistent in terms of variables, causal assumptions or parameterization. The approach involves assessing whether competing models of legal arguments are explained or predict facts uncovered before or during the trial process. Those models that are more heavily disconfirmed by the facts are given lower weight, as model plausibility measures, in the Bayesian model comparison and averaging framework adopted. In this way a plurality of arguments is allowed yet a single judgement based on all arguments is possible and rational.

Implementation of a Curriculum-Integrated Computer Game for Introducing Scientific Argumentation.

Argumentation has been emphasized in recent U.S. science education reform efforts (NGSS Lead States 2013; NRC 2012), and while existing studies have investigated approaches to introducing and supporting argumentation (e.g., McNeill & Krajcik 2008; Kang, Thompson &Windschitl 2014), few studies have investigated how game-based approaches may be used to introduce argumentation to students. In this paper, we report findings from a design-based study of a teacher's use of a computer game intended to introduce the Claim, Evidence, Reasoning (CER) framework (McNeill &Krajcik 2012) for scientific argumentation. We studied the implementation of the game over two iterations of development in a high school biology teacher's classes. The results of this study include aspects of enactment of the activities and student argument scores. We found the teacher used the game in aspects of explicit instruction of argumentation during both iterations, although the ways in which the game was used differed. Also, students' scores in the second iteration were significantly higher than the first iteration. These findings support the notion that students can learn argumentation through a game, especially when used in conjunction with explicit instruction and support in student materials. These findings also highlight the importance of analyzing classroom implementation in studies of game-based learning.

Learning to argue via apprenticeship.

We examined apprenticeship, in the form of interaction with a more capable other, as a mechanism of development of higher-order reasoning skills, specifically argumentation. Over a 1-year period, middle school students engaged in twice-weekly electronic dialogs with a sequence of different peers on a series of social issues. In one group, unbeknownst to participants, a highly capable adult substituted for peers in half of their dialogs. Beginning immediately, increasing with time, and extending to peer-only dialogs on a new topic, the quality of argumentation shown by the experimental group exceeded that of a comparison peer-only group, highlighting the power of apprenticeship as a mechanism in the development of reasoning, a demonstration of both theoretical and applied significance.