Humboldt-Universität zu Berlin

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Research Units at Humboldt-Universität zu Berlin

FOR 2265: Law - Gender - Collectivity: Processes of standardization, categorization and generating solidarity

Among the most contentious issues of western democracies are questions about belonging and participation. Law plays a central role here - either as a reference point for the formulation of claims, or as a goal for the shifting of existing borders. Out of this dynamic, new social conflicts, such as about antidiscrimination laws and the rights of workers, illustrate to whom the state belongs, about the rights of fleeing persons, and broadly about the possibility of equal participation for all in the practice of civic autonomy (Jürgen Habermas). Against this backdrop, the interdisciplinary research group (FOR) will focus on the relationship between law, sex/gender and collectivity. We ask about the efficacy of gendered collectivity in a hegemonic male-oriented, hetero-normative, bourgeois and privatized tradition of law. With a deepened understanding of collectivization processes that are both legally standardized and gendered, we are interested in how current social conflicts present themselves, and how they may be understood and described with due complexity. To this end, the FOR project accounts for multiple dimensions of collectivity - (social) collectives, ideas of collectivity, and processes of collectivization. It focuses on the intersections of law as a meaningful, socio-cultural field of discourse and activity, and sex/gender as a powerful social norm and structural category. The FOR goals are thus, firstly, added theoretical value for the fields of both legal studies and gender studies, and, with this specific focus, secondly, achieving a unique contribution to understandings of the meaning of collectivity in late modern societies from a transnational perspective.

Spokesperson:
Prof. Dr. Beate Binder
Faculty of Arts and Humanities
Department of European Ethnology
Mohrenstr. 40/41
D-10117 Berlin
E-Mail: beate.binder@hu-berlin.de

Duration: 2017-
 

 

FOR 2537 Emerging grammars in language contact situations: A comparative approach (RUEG)

The Research Unit “Emerging Grammars in Language Contact Situations: A Comparative Approach” (in short: “RUEG”) plans to investigate the linguistic systems and linguistic resources of bilingual speakers from families with an immigrant history, “heritage speakers”, in both of their languages (heritage and majority language) across formal and informal, written and spoken communicative situations. Taking a distinctly competence-oriented perspective on linguistic repertoires, we will study noncanonical phenomena as indicators of new grammatical options, and analyse their grammatical structure. We will investigate speakers of Russian, Turkish, and Greek as heritage languages in Germany and the U.S., in addition to German as a heritage language in the US, as well as monolingual controls for majority and heritage languages. We will collect data using a unified methodology in order to elicit comparable naturalistic data from different registers (“Language Situations”). This data will be integrated in a shared corpus, and analysed comparatively in close collaboration among the different projects. All projects will contribute to three “Joint Ventures”. These Joint Ventures organise research activities in RUEG and are guided by three key hypotheses that provide the overall conceptual frame for investigations in all projects. By doing so, we will target (1) the development of new dialects vs. incomplete acquisition or erosion (“Language Change Hypothesis”), (2) the relevance of external vs. internal grammatical interfaces (“Interface Hypothesis”), and (3) the distinction of contact-induced vs. language-internal change and variation (“Internal Dynamics Hypothesis”). As a result of our collaborative research, we expect new insights into the special dynamics of language variation, language change, and linguistic repertoires in contact situations; the modelling of noncanonical structures in the grammatical system; and new impulses for the investigation of heritage speakers and speakers’ resources in general.

Spokesperson:
Prof. Dr. Heike Wiese
Faculty of Language, Literature and Humanities
Department of German Studies and Linguistics
Unter den Linden 6
D-10099 Berlin
E-Mail: heike.wiese@hu-berlin.de 

Duration: 2018-

 

 

FOR 2569 Agricultural Land Markets - Efficiency and Regulation (FORLand)

Growing food prices and high liquidity in international financial markets have boosted the demand for land. As a result, agricultural land prices have steadily increased over the past decade in many parts of the world. This development has fueled public debates on whether land markets as an allocation device work efficiently and whether current legislation is in line with political objectives and societal needs. Despite existing research, the current state of knowledge on land-related topics remains fragmented and does not provide clear answers to this debate. The proposed research unit FORLand pursues two overall objectives. First, we aim to evaluate the outcome of land markets and verify whether they fulfill their societal functions or whether adapted policy interventions are needed. To this end, we take a comprehensive view of recent developments on agricultural land markets and their drivers. Our second objective is to evaluate existing and proposed policy instruments to inform discussion on the design of optimal regulation. This involves an empirical (ex-post) evaluation of existing regulations in conjunction with the development of theoretical (microeconomic) models. We focus on developed economies (including mature and transition countries) since in many developing and emerging countries land markets have not been completely established and face different challenges. The complexity of factors that determine the outcome of land markets calls for a multidisciplinary approach including (agricultural) economics, sociology, ethics, geography and statistics. Apart from direct outcomes such as prices and their spatial and regional patterns, we also consider indirect outcomes such as land use because they affect important societal concerns. To carve out the impact that land market regulation has on land market outcomes we have to control for external factors, particularly urban sprawl and the developments on financial markets. Moreover, the interplay with other policy instruments, particularly agricultural, energy and environmental policies, has to be taken into account. Complementary to the economic perspective, we discuss equality issues of land ownership from a sociological and ethical perspective and consider environmental outcomes of land market dynamics. The outcome of this project will be particularly beneficial for transition countries that currently face the task of establishing agricultural land markets.

Spokesperson:
Prof. Dr. Martin Oedning
Faculty of Life Sciences
Albrecht Daniel Thaer-Institute of Agricultural and Horticultural Sciences
Unter den Linden 6
D-10099 Berlin
E-Mail: m.odening@agrar.hu-berlin.de 

Duration: 2017-

 

 

FOR 5187 Towards precision psychotherapy for non-respondent patients: From signatures to predictions to clinical utility

Although cognitive-behavioral therapy (CBT) is a first-line treatment for internalizing disorders, a substantial proportion of patients fails to benefit - with severe consequences for patients and costs for societies. Precision mental health can help to identify patients at risk for non-response (NR) already prior to treatment initialization. The paucity of standard clinical features that allow for single-case predictions serves as an impetus to search for additional layers of NR. The work pro-gram of this Research Unit (RU) will foster the development of precision psychotherapy by i) in-vestigating clinical and bio-behavioral signatures of NR to improve our understanding of this phenomenon, ii) applying state-of-the-art machine learning technology for single-case predic-tions, and iii) validating these for clinical utility in an ecologically valid treatment setting, bring-ing together four major academic outpatient clinics in Berlin. Our effort will thus pave the way for a priori patient stratification to intensified or augmented treatments in a putative second funding period. To achieve this, we will set up a prospective-longitudinal multicenter observational study on n = 500 patients with internalizing disorders (specific phobia, social anxiety disorder, panic disorder, agoraphobia, generalized anxiety disorder, obsessive-compulsive disorder, post-traumatic stress disorder, unipolar depressive disorders) who will be deeply phenotyped prior to CBT using hypotheses-based clinical, e-mental health, psychophysiological and neuroimaging measures. Assessment batteries and treatment documentation will be harmonized across cen-ters. Predictive analytics will be provided by our methods platform, including computer vision algo-rithms such as convolutional neural networks, multiple kernel and transfer learning and an infra-structural basis (hard- and software, data management plans, high-performance computing). The RU aims to significantly improve the field by 1) setting up a multilevel and -method assessment battery to search for the best predictors, combinations thereof, and cost-efficient proxies, 2) in-vestigating bio-behavioral signatures of emotion regulation as a putative key mechanism of CBT, 3) applying a transdiagnostic focus on NR signatures, 4) within one comprehensive sample that exerts a high degree of ecological validity, thus fostering translation to clinical practice with diverse patient characteristics. These goals can only be achieved by concerted ac-tion of experts in the fields of clinical psychology, psychotherapy, e-mental health, psychophysiol-ogy, cognitive neuroscience, and neuroinformatics. We will maximize synergies with large-scale consortia (UK Biobank, ENIGMA, CRC-TRR 58, BMBF psychotherapy initiative, PING, KODAP). This RU will make substantial progress in answering the question how we can better under-stand the phenomenon of NR, identify and address this vulnerable and cost-intensive group of NR patients.

Spokesperson:
Prof. Dr. Ulrike Lüken
Faculty of Life Sciences
Instiute of Psychology
Unter den Linden 6
D-10099 Berlin
E-Mail: ulrike.lueken@hu-berlin.de

Duration: 2022-

 

 

FOR 5363 KI-FOR Fusing Deep Learning and Statistics towards Understanding Structured Biomedical Data

High-throughput measurements in the biomedical sciences such as stacks of images, genome sequences or time-series constitute structured data that are characterized by their inherent dependencies between measurements, often non-vectorial nature and the presence of confounding influences and sampling biases. For example, population structure, systematic measurement artifacts, non-independent sampling or different group age distributions can lead to spurious results if not accounted for. Deep learning excels in many applications on structured data due to the ability to capture complex dependencies within and between inputs and outputs, allowing for accurate prediction. Despite recent advances in explainable artificial intelligence and Bayesian neural networks, deep learning still has limitations with respect to its assessment of uncertainty, interpretability, and validation. These, however, are important components in order to go beyond prediction towards understanding the underlying biology. To this end, statistics has traditionally been used in the biomedical sciences due to interpretable model output and statistical inference, which i.a. provides quantification of uncertainty, corrections for confounding and testing of hypotheses with statistical error control. Methods from classical statistics, however, have limitations in their modelling flexibility for structured data and their ability to capture complex non-linearities in a data-driven way.In this research unit we bring together experts from machine learning and statistics with a track record in biomedical applications to address the following overarching objectives:(O1) to integrate deep learning and statistics to improve interpretability, uncertainty quantification and statistical inference for deep learning, and to improve modeling flexibility of statistical methods for structured data. In particular, we will develop methods that provide statistical inference for structured data by quantification of uncertainty, testing of hypotheses and conditioning on confounders, and that improve explanations of structured data through hybrid statistical and deep learning models, population- and distribution-level explanations, and robust sparse explanations.(O2) to create a feedback loop between this methods development and biomedical applications, where we account for the needs in the analysis of the data when developing new methods and generate biomedical insights from applications of the developed methods to the data. Applications include analysis of MRI, fMRI and microscopy images, longitudinal disease progression modeling, DNA sequence analysis, and genetic association studies.

Spokesperson:
Prof. Dr. Sonja Greven
Faculty of Economics and Business
Unter den Linden 6
D-10099 Berlin
E-Mail: sonja.greven@hu-berlin.de

Duration: 2023-