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„Bildung als Selbstzweck, yeih!“ Tut!-Evaluation Sommersemester 2026 Ergebnisse
(2026-09) Kraatz, Carl Eberhard
Die Tutorien des Tutorienprogramms Tut! werden von den Teilnehmenden im Nachhinein in einer anonymen Evaluationsbefragung bewertet. Der Ergebnisbericht gibt Auskunft über die entsprechenden Einschätzungen. Die Ergebnisse sind Grundlage für die Weiterentwicklung des studentisch organisierten und selbstbestimmten Tutorienprogramms. Im Bericht werden die Ergebnisse aus der Befragung Sommersemesters 2026 (N=35) dargestellt. Teilweise werden sie durch Daten aus der Tut!-Anmeldestatistik bzw. aus der Studierendenbefragung (JLU 2026) ergänzt/kontrastiert.
From Dissonance to Resonance – Reverberations of Public Memory in Post-Colonial Portugal
(2026) Thaler, Miriam Theresa
This thesis analyses the complexities and challenges in how contemporary Portugal publicly remembers its colonial past and asks: how can liberal democratic societies navigate conflicting memory narratives without enforcing an undemocratic choice of a single storyline? These challenges are highlighted through two particularly fervent flashpoints, extending roughly through the years 2017-2020, that crystallized disagreements in the public memoryscape. These sprang from debates around the installation of the statue of the 17th-century Jesuit Padre António Vieira in Lisbon as well as the proposal to build in the capital a Museum of Discovery, dedicated to the Portuguese era of maritime expansion. The thesis reads these problem cases alongside two recent artistic interventions: Kiluanji Kia Henda’s Plantação (Plantation), a proposal for a Lisbon memorial to the enslaved emerging from a 2017 initiative by the non-governmental organisation DJASS, and Grada Kilomba’s O Barco (The Boat, 2021), a performance piece that centres on the remembrance of slavery as well. In distinct ways they provide pathways to resolving these challenges through resonant narrations. Methodologically, the thesis combines discourse analysis of public debate, mainly opinion pieces but also press, policy statements, speeches and civic initiatives, with close readings of the two artworks. Theoretically, it goes beyond Aleida Assmann’s canon/archive framework and draws on Nietzschean language and his distinction between monumental and critical history, supplemented by narratological attention to differences in modes of remembering. The central argument is that ethical, democratic remembering requires cultivating affective resonance across divergent narratives rather than enforcing narrative or affective consensus. The two artworks are shown to open concrete pathways for such resonances by potentially expanding the mnemonic canon and allowing for plural forms of remembrance. In doing so, they model practices through which liberal democracies can acknowledge historical violence while sustaining narrative and affective plurality. Beyond Portugal, the findings offer transferable methods for reconciling conflicted memory narratives and for designing inclusive public memory that underwrites and reinforces democratic citizenship.
Ökonomische Bewertung von Futterbaufruchtfolgen im Spannungsfeld hoher Milchleistungen und der Flächenkonkurrenz von Futter- und Nahrungsmitteln
(2026) Friedrich, Kathrin
Die vorliegende Arbeit wurde zur Erlangung des Master of Science im Studiengang Agrar- und Ressourcenökonomie geschrieben und basiert auf dem am Gladbacher Hof (Lehr- und Versuchsbetrieb) durchgeführten "Green Dairy Projekt". Es wird das Spannungsfeld zwischen hoher Milchleistungen, welche mit einem höheren Feldfutterbau einhergehen, und geringeren Milchleistungen ökonomisch bewertet. Bei Letzterem wird eine vornehmliche Grünlandfütterung forciert, um auf Ackerflächen auf direkte Weise Nahrungsmittel anzubauen. Neben der ökonomisch/ monetären Dimension werden die beiden Systeme auch hinsichtlich ihrer netto-verfügbaren humanernährungstauglichen Mengen an Proteinen und Energie ausgewertet. Die ganze Arbeit unterliegt den Bedingungen des Ökolandbaus.
Experimental Approaches to the Corey-Bakshi-Shibata and Dakin-West Reactions as Model Systems for Machine Learning Applications in the Small-Data Regime
(2026) Pereira, Oliver Simão
Predicting enantioselectivity remains a fundamental challenge in asymmetric organocatalysis. While Machine Learning (ML) offers data-driven pathways for catalyst optimization, its application is hindered by data scarcity and publication bias. This work investigates the integration of experimental data into predictive ML models within the small-data regime, using the Corey-Bakshi-Shibata (CBS) reduction and the asymmetric variant of the Dakin-West reaction as model reactions. The primary objective was the development of robust predictive models from limited, unbiased datasets, and to delineate the operational limits of current molecular representations as well as ML algorithms. Initial investigations of the CBS reduction of ketones yielded a graph-based ML model that predicts stereochemical outcomes and identifies promising catalyst precursors. However, the synthesis and investigation of additional precursors, enabled by a newly developed synthetic route, revealed deviations from previous predictions. This demonstrates that the reliability of small-data ML models depends on high-quality datasets and robust analytics to avoid the compensation for empirical artifacts. Further investigations of CBS catalysts into ketimine substrates revealed that an unselective background reaction consistently outpaces the catalyzed pathway. This inherent lack of stereocontrol renders ketimines unsuitable for modeling, making the suppression of the background pathway an essential prerequisite for future work. For the asymmetric Dakin-West reaction, the research strategy prioritized empirical data generation over premature modeling. Through systematic parameter optimization, a comprehensive data set was established, which maps complex structure-activity relationships and provides a solid, high-quality foundation for future ML applications. Ultimately, this work demonstrates that advancing ML-powered catalyst design within the small-data regime depends on the synergy of high-precision experimental data and rigorous analytical validation. By focusing on data quality in addition to increasing data quantity, the operational boundaries of predictive modeling can be pushed to address complex synthetic challenges.
An empirical three-dimensional metric field for color space
(2026) Koenderink, Jan J.; van Doorn, Andrea J.; Braun, Doris I.; Gegenfurtner, Karl R.
A complete empirical characterization of color discrimination in three dimensions has long remained out of reach. Classical studies, beginning with MacAdam's ellipses, provided local measurements in restricted chromatic planes, but a spatially dense and internally consistent mapping of discrimination structure across full color space has not yet been achieved. Here, we present such a systematic three-dimensional measurement of color discrimination in the standardized sRGB stimulus space. Eight observers measured discrimination regions at 35 reference colors distributed on a body-centered cubic lattice within the RGB cube. At each location, color differences were probed along seven orientations, yielding 14 directional extents. These measurements defined centrally symmetric convex regions that were fitted with minimum-volume ellipsoids, providing a compact description of local discrimination structure. Ellipsoids were represented as symmetric positive-definite matrices and analyzed using a Frobenius geometry, enabling normalization across observers and smooth interpolation to arbitrary locations. The resulting metric field is spatially smooth, highly structured, and remarkably consistent across observers up to an individual global scale factor. Grain size increases along the achromatic axis and exhibits systematic chromatic asymmetries. Comparison with CIEDE2000 reveals substantial agreement in overall scale variation but systematic differences in local anisotropy. Because the metric field is defined geometrically, it can be transformed into linear RGB or any other color representation through the corresponding coordinate transformations. Together, these data provide a coherent three-dimensional empirical mapping of color discrimination across a large ecologically relevant volume of color space and establish an empirical framework for perceptual color metrics.