Preconditions, Limits and Chances of Systemic Constructivist Thinking in Intercultural Mediation

Interkulturelle Mediation hat sich in der Gegenwart zu einem der wichtigsten Themenfelder der Mediation entwickelt. Es besteht für sie ein hoher Bedarf vor dem Hintergrund sozialer, gesellschaftlicher und politischer Veränderungen. Deswegen ist eine Beschäftigung mit den Grundlagen der Mediationsarbeit und ihren theoretischen Voraussetzungen, Methoden und Termini für dieses Gebiet der Mediation sinnvoll und von grundlegender Bedeutung. In dieser Masterarbeit soll die Rolle der systemisch-konstruktivistischen Theorie als Grundlage der Mediation näher betrachtet werden. Sie zeigt, dass seine Grundannahmen wissenschaftshistorisch einer bestimmten Richtung des Denkens zugeordnet werden können, die sich Mitte bis Ende des 20. Jahrhunderts herausgebildet hat. Erkenntnistheoretisch betrachtet schafft dieser Denkansatz Realitäten, denen wir uns bewusst sein müssen, um der Praxis der interkulturellen Mediation gerecht werden zu können. Nachdem das Thema Voraussetzungen, Grenzen und Chancen des systemisch-konstruktivistischen Denkens in der interkulturellen Mediation bisher unter philosophischen Gesichtspunkten wissenschaftlich noch nicht untersucht und systematisch analysiert wurde, soll diese Masterarbeit diese Lücke schließen. Sie stellt eine Kritik des systemischen Konstruktivismus unter besonderer Berücksichtigung der interkulturellen Mediation in zweierlei Hinsicht dar: Der systemische Konstruktivismus und in Folge das Harvard Negotiation Project, das sich auf diesen Denkansatz bezieht und derzeit noch die Hauptmethode für Mediatoren in Ausbildung, Theorie und Praxis darstellt, werden auf ihre philosophischen Vorannahmen überprüft. Die Ergebnisse einer Mediationspraxis, die auf solchen Axiomen beruht, können nur eingeordnet und verstanden werden, wenn sie die Fragen, die eine philosophische Reflexion und Analyse auslöst, zulässt

Preliminary test estimation for the second order autoregression / 1992:107

Includes bibliographical references (p.17)

Analog to Digital : Transitions in Theory and Practice in Archaeological Photography at Çatalhöyük

Archaeology and photography has a long, co-constructed history that has increasingly come under scrutiny as archaeologists negotiate the visual turn. Yet these investigations do not make use of existing qualitative and quantitative strategies developed by visual studies to understand representation in archaeological photographs. This article queries the large photographic archive created by ongoing work at the archaeological site of Çatalhöyük in Turkey to consider the visual impact of changing photographic technologies and of a shifting theoretical focus in archaeology. While using content analysis and semiotic analysis to gain a better understanding of the visual record, these analyses also unexpectedly reveal power dynamics and other social factors present during archaeological investigation. Consequently, becoming conversant in visual analyses can contribute to developing more reflexive modes of representation in archaeology

Cognitive‐behavioral therapy in the time of coronavirus : clinician tips for working with eating disorders via telehealth when face‐to‐face meetings are not possible

Objective The coronavirus pandemic has led to a dramatically different way of working for many therapists working with eating disorders, where telehealth has suddenly become the norm. However, many clinicians feel ill equipped to deliver therapy via telehealth, while adhering to evidence‐based interventions. This article draws together clinician experiences of the issues that should be attended to, and how to address them within a telehealth framework. Method Seventy clinical colleagues of the authors were emailed and invited to share their concerns online about how to deliver cognitive‐behavioral therapy for eating disorders (CBT‐ED) via telehealth, and how to adapt clinical practice to deal with the problems that they and others had encountered. After 96 hr, all the suggestions that had been shared by 22 clinicians were collated to provide timely advice for other clinicians. Results A range of themes emerged from the online discussion. A large proportion were general clinical and practical domains (patient and therapist concerns about telehealth; technical issues in implementing telehealth; changes in the environment), but there were also specific considerations and clinical recommendations about the delivery of CBT‐ED methods. Discussion Through interaction and sharing of ideas, clinicians across the world produced a substantial number of recommendations about how to use telehealth to work with people with eating disorders while remaining on track with evidence‐based practice. These are shared to assist clinicians over the period of changed practice

Presence of depression and anxiety before and after coronary artery bypass graft surgery and their relationship to age

<p>Abstract</p> <p>Background</p> <p>Scientific literature on depression and anxiety in patients with coronary heart disease (CHD) consistently reports data of elevated anxiety and depression scores indicating clinically relevant quantities of these psychopathological conditions. Depression is considered to be a risk factor for the development of CHD and deteriorates the outcome after cardiac rehabilitation efforts. The aim of our study was to evaluate the presence of clinically relevant anxiety and depression in patients before and after coronary artery bypass grafting (CABG). Additionally we evaluated their relationship to age because of the increasing number of elderly patients undergoing CABG surgery.</p> <p>Methods</p> <p>One hundred and forty-two consecutive patients who underwent CABG in our hospital were asked to fill in the "Hospital Anxiety and Depression Scale – German Version (HADS)" to measure depression and anxiety scores two days before and ten days after CABG surgery. Differences between these pre- and post-surgical scores were then calculated as means for changes, and the amount of elevated scores were appraised. In order to investigate the relationship between age and anxiety and depression, respectively, Spearman correlations between age and the difference scores were calculated. In addition, ANOVA procedures with the factor "age group" and McNemar tests were calculated. Therefore the sample was divided into four equally sized age groups.</p> <p>Results</p> <p>25.8% of the patients were clinically depressed before and 17.5% after surgery; 34.0% of the patients were clinically anxious before and 24.7% after surgery. This overall change is not significant. We found a significant negative correlation between age and the difference between the two time points for anxiety (Spearman rho = -.218; p = 0.03), but not for depression (Spearman rho = -.128; p = 0.21). ANOVA and McNemar-Tests revealed that anxiety scores and the number of patients high in anxiety declined statistically meaningful only in the youngest patient group. Such a relationship could not be found for depression.</p> <p>Conclusion</p> <p>Our data show a relationship between age and anxiety. Younger patients are more anxious before CABG surgery than older ones and show a decline in symptoms while elderly patients show hardly any change.</p

Causality guided machine learning model on wetland CH4 emissions across global wetlands

Wetland CH4 emissions are among the most uncertain components of the global CH4 budget. The complex nature of wetland CH4 processes makes it challenging to identify causal relationships for improving our understanding and predictability of CH4 emissions. In this study, we used the flux measurements of CH4 from eddy covariance towers (30 sites from 4 wetlands types: bog, fen, marsh, and wet tundra) to construct a causality-constrained machine learning (ML) framework to explain the regulative factors and to capture CH4 emissions at sub -seasonal scale. We found that soil temperature is the dominant factor for CH4 emissions in all studied wetland types. Ecosystem respiration (CO2) and gross primary productivity exert controls at bog, fen, and marsh sites with lagged responses of days to weeks. Integrating these asynchronous environmental and biological causal relationships in predictive models significantly improved model performance. More importantly, modeled CH4 emissions differed by up to a factor of 4 under a +1C warming scenario when causality constraints were considered. These results highlight the significant role of causality in modeling wetland CH(4 )emissions especially under future warming conditions, while traditional data-driven ML models may reproduce observations for the wrong reasons. Our proposed causality-guided model could benefit predictive modeling, large-scale upscaling, data gap-filling, and surrogate modeling of wetland CH4 emissions within earth system land models

Causality guided machine learning model on wetland CH4 emissions across global wetlands

Wetland CH4 emissions are among the most uncertain components of the global CH4 budget. The complex nature of wetland CH4 processes makes it challenging to identify causal relationships for improving our understanding and predictability of CH4 emissions. In this study, we used the flux measurements of CH4 from eddy covariance towers (30 sites from 4 wetlands types: bog, fen, marsh, and wet tundra) to construct a causality-constrained machine learning (ML) framework to explain the regulative factors and to capture CH4 emissions at sub -seasonal scale. We found that soil temperature is the dominant factor for CH4 emissions in all studied wetland types. Ecosystem respiration (CO2) and gross primary productivity exert controls at bog, fen, and marsh sites with lagged responses of days to weeks. Integrating these asynchronous environmental and biological causal relationships in predictive models significantly improved model performance. More importantly, modeled CH4 emissions differed by up to a factor of 4 under a +1C warming scenario when causality constraints were considered. These results highlight the significant role of causality in modeling wetland CH(4 )emissions especially under future warming conditions, while traditional data-driven ML models may reproduce observations for the wrong reasons. Our proposed causality-guided model could benefit predictive modeling, large-scale upscaling, data gap-filling, and surrogate modeling of wetland CH4 emissions within earth system land models.Peer reviewe

Causality guided machine learning model on wetland CH4 emissions across global wetlands

Wetland CH4 emissions are among the most uncertain components of the global CH4 budget. The complex nature of wetland CH4 processes makes it challenging to identify causal relationships for improving our understanding and predictability of CH4 emissions. In this study, we used the flux measurements of CH4 from eddy covariance towers (30 sites from 4 wetlands types: bog, fen, marsh, and wet tundra) to construct a causality-constrained machine learning (ML) framework to explain the regulative factors and to capture CH4 emissions at sub -seasonal scale. We found that soil temperature is the dominant factor for CH4 emissions in all studied wetland types. Ecosystem respiration (CO2) and gross primary productivity exert controls at bog, fen, and marsh sites with lagged responses of days to weeks. Integrating these asynchronous environmental and biological causal relationships in predictive models significantly improved model performance. More importantly, modeled CH4 emissions differed by up to a factor of 4 under a +1C warming scenario when causality constraints were considered. These results highlight the significant role of causality in modeling wetland CH(4 )emissions especially under future warming conditions, while traditional data-driven ML models may reproduce observations for the wrong reasons. Our proposed causality-guided model could benefit predictive modeling, large-scale upscaling, data gap-filling, and surrogate modeling of wetland CH4 emissions within earth system land models.Peer reviewe