Assessing public awareness of social justice documentary films based on news coverage versus social media

The comprehensive measurement of the impact that information products have on individuals, groups and society is of practical relevance to many actors, including philanthropic funding organizations. In this paper we focus on assessing one dimension of impact, namely public awareness, which we conceptualize as the amount and substance of attention that information products gain from the press and social media. We are looking at a type of products that philanthropic organizations fund, namely social justice documentaries. Using topic modeling as a text summarization technique, we find that films from certain domains, such as “Politics and Government” and “Environment and Nature,” attract more attention than productions on others, such as “Gender and Ethnicity”. We also observe that film-related public discourse on social media (Facebook and non-expert reviews) has a higher overlap with the content of a film than press coverage of films does. This is partially due to the fact that social media users focus more on the topics of a production whereas the press pays strong attention to cinematographic and related features

Commoning the food system: Barriers, opportunities and resilience strategies on the case of CampiAperti, Bologna, Italy

The concept of ‘Food sovereignty’ was articulated by the global peasant movement La Via Campesina in 1994, in response to the neo-liberalisation of agriculture. Most academic research on food sovereignty focusses on the global South, and only little attention has been paid to the European peasant movement and their strategies to build food sovereignty in a context in which, according to European La Via Campesina, the EU Common Agricultural Policy is putting a small farm out of business every three minutes, and agro-industry emits one fourth of all carbon emissions in the continent. This thesis discusses the transformative potential of food production and the decommodification of foodstuff from a commons and commoning perspective. Analysing the case of CampiAperti, a producer Association in Bologna, Italy, I demonstrate multiple production systems in use-value through the lens of the peasant condition where farmers have taken ownership over the production stages of their selected craft, and through commoning have put in place an agroecological value system based on animal and labour rights. In exerting their value system, two autopoietic mechanisms were developed to assert their ecological and social boundaries from the state, capitalist system and free-riders. The first one is the participatoryguarantee-system (PGS), and the second is the collaborative price-mechanism (CPM). The PGS is instrumental to self-certifying their foodstuff, which raises the critical question of boundaries and enclosures from a commons perspective. While the CPM is used to eliminate competitive behaviour amongst producers by setting their own ‘just prices’. This mechanism is scrutinised on competition, and on the tension between guaranteeing a livelihood for farmer and the affordability of their foodstuff for consumers. Both PGS and CPM mechanism defy the capitalist logic of neo-liberalisation of the food system as well as the logics of the Common Agricultural Policy (CAP), and thus these mechanisms are strategic political tools to emancipate from the capitalist food market and are employed to self-govern their own markets. Foodstuff is evaluated as a common good, arguing that the created food system is a closed commons circuit.  Conducting fieldwork on farms, markets, and assemblies, the study addresses the possibility of materialising food sovereignty by examining production and distribution of foodstuff in usevalue. It utilises a practice-centred approach and draws on a mixed-method, multi-sited ethnographic strategy to explore how individuals take responsibility of their re/production and examines the producer’s commitment to participate in self-governing the food system through commoning. The ethnographic study is supplemented with a discourse and conversational analysis to get a deeper understanding of CampiAperti’s organisation and of their complex horizontal governance structure

PyTAIL: Interactive and Incremental Learning of NLP Models with Human in the Loop for Online Data

Online data streams make training machine learning models hard because of distribution shift and new patterns emerging over time. For natural language processing (NLP) tasks that utilize a collection of features based on lexicons and rules, it is important to adapt these features to the changing data. To address this challenge we introduce PyTAIL, a python library, which allows a human in the loop approach to actively train NLP models. PyTAIL enhances generic active learning, which only suggests new instances to label by also suggesting new features like rules and lexicons to label. Furthermore, PyTAIL is flexible enough for users to accept, reject, or update rules and lexicons as the model is being trained. Finally, we simulate the performance of PyTAIL on existing social media benchmark datasets for text classification. We compare various active learning strategies on these benchmarks. The model closes the gap with as few as 10% of the training data. Finally, we also highlight the importance of tracking evaluation metric on remaining data (which is not yet merged with active learning) alongside the test dataset. This highlights the effectiveness of the model in accurately annotating the remaining dataset, which is especially suitable for batch processing of large unlabelled corpora. PyTAIL will be available at https://github.com/socialmediaie/pytail.Comment: 9pages, 3 figures, 2 table

Qualitive and quantitive mass spectrometric analysis of neuroactive substances from single insect neurons

Organisms need to constantly adapt their behavior to the changing environment as well as react towards changes in their internal state. The nervous system perceives and processes such stimuli and coordinates the corresponding reactions of the body. This system is based on regulated cell-cell communication, utilizing a wide range of different chemical signaling molecules and receptors. If one wants to fully grasp how neural circuits process, modulate and relay incoming information, then the involved neuroactive substances, their cellular distribution, temporal and quantitative dynamics have to be analyzed on single cell resolution. Single cell mass spectrometry (SCMS) allows the interrogation of chemical profiles from individual cells, including neuroactive substances such as neuropeptides and biogenic amines. Matrix assisted laser desorption/ionization – time-of-flight mass spectrometry (MALDI-TOF MS) has established itself as a fast and reliable tool for the analysis of neuropeptides from single neurons of invertebrates and vertebrates alike. However, the detection of small signaling molecules, such as biogenic monoamines, by MALDI-TOF SCMS has been challenging. Biogenic monoamines play key roles in orchestrating and modulating neural circuits, therefore a MALDI-TOF SCMS based method for their detection and quantification is highly desirable. Additionally, biogenic monoamines can be co-localized with neuropeptides. Therefore the development of a MALDI-TOF SCMS based method capable of detecting both neuroactive substances would help to reveal such overlapping expression profiles. In the current thesis, I focused on the development of a MALDI-TOF SCMS based method that allows the detection and quantification of biogenic monoamines from single somata of insect neurons. The study focused on the insect octopaminergic/tyraminergic system, with an emphasis on octopamine (OA), which is considered to be homologous to the vertebrate noradrenalin/adrenalin system. By using chemical derivatization of amine moieties of OA and tyramine (TA) and an optimized sample preparation, I was able to lower the respective detection limits to single cell concentrations. Additionally, I could show that the chemical derivatization does not interfere with the detection of neuropeptides from the same sample, hence allowing the simultaneous detection of both substance classes. Further, I could show that absolute quantification of OA and TA is possible from single cell sample volumes using isotopically labeled synthetic standards. I used the developed protocol for the qualitative and quantitative analysis of OA/TA from genetically labeled and manually microdissected somata of interneurons from the fruit fly Drosophila melanogaster. Using the newly developed approach, I analyzed intracellular OA/TA ratios, compared somatic OA titers between sexes and two different OAergic cell clusters and revealed that prolonged cooling of animals has an increasing effect on detectable OA titers in the analyzed neurons. Furthermore, I used the developed protocol to analyze changes in somatic OA titers of aggression modulating OAergic neurons from the gnathal ganglion in socially naive and experienced adult male D. melanogaster. I could show that the somatic OA titer increases in these neurons when flies had social contact with the same sex compared to naive flies, which is possibly mediated by an input from pheromone detecting gustatory receptor neurons. To my knowledge, this is the first study to report a quantified increase of a somatic biogenic monoamine titer detected directly from individual isolated neurons of intact insect brains between two behavioral states by mass spectrometric analysis. In a collaborative study, I employed the developed protocol to intracellular recorded descending dorsal unpaired median neurons from the Indian stick insect Carausius morosus and was able to confirm that these neurons contain OA and TA and thus could be OAergic. Finally, as a starting point in an effort to create a map of neuropeptidergic neurons and their repertoire of neuroactive substances in adult D. melanogaster, I was involved in the analysis of single genetically labeled neuropeptidergic neuron somata using MALDI-TOF SCMS. In summary, we could describe a total of 10 different cell types characterized by their expressed neuropeptides and their location in the CNS. Future studies will focus on analyzing these cell types towards potential co-localized aminergic transmitters using the developed protocol

StereoMap: Quantifying the Awareness of Human-like Stereotypes in Large Language Models

Large Language Models (LLMs) have been observed to encode and perpetuate harmful associations present in the training data. We propose a theoretically grounded framework called StereoMap to gain insights into their perceptions of how demographic groups have been viewed by society. The framework is grounded in the Stereotype Content Model (SCM); a well-established theory from psychology. According to SCM, stereotypes are not all alike. Instead, the dimensions of Warmth and Competence serve as the factors that delineate the nature of stereotypes. Based on the SCM theory, StereoMap maps LLMs' perceptions of social groups (defined by socio-demographic features) using the dimensions of Warmth and Competence. Furthermore, the framework enables the investigation of keywords and verbalizations of reasoning of LLMs' judgments to uncover underlying factors influencing their perceptions. Our results show that LLMs exhibit a diverse range of perceptions towards these groups, characterized by mixed evaluations along the dimensions of Warmth and Competence. Furthermore, analyzing the reasonings of LLMs, our findings indicate that LLMs demonstrate an awareness of social disparities, often stating statistical data and research findings to support their reasoning. This study contributes to the understanding of how LLMs perceive and represent social groups, shedding light on their potential biases and the perpetuation of harmful associations.Comment: Accepted to EMNLP 202

Examining the Causal Effect of First Names on Language Models: The Case of Social Commonsense Reasoning

As language models continue to be integrated into applications of personal and societal relevance, ensuring these models' trustworthiness is crucial, particularly with respect to producing consistent outputs regardless of sensitive attributes. Given that first names may serve as proxies for (intersectional) socio-demographic representations, it is imperative to examine the impact of first names on commonsense reasoning capabilities. In this paper, we study whether a model's reasoning given a specific input differs based on the first names provided. Our underlying assumption is that the reasoning about Alice should not differ from the reasoning about James. We propose and implement a controlled experimental framework to measure the causal effect of first names on commonsense reasoning, enabling us to distinguish between model predictions due to chance and caused by actual factors of interest. Our results indicate that the frequency of first names has a direct effect on model prediction, with less frequent names yielding divergent predictions compared to more frequent names. To gain insights into the internal mechanisms of models that are contributing to these behaviors, we also conduct an in-depth explainable analysis. Overall, our findings suggest that to ensure model robustness, it is essential to augment datasets with more diverse first names during the configuration stage

Kooperation der Lernorte in der beruflichen Bildung (KOLIBRI). Abschlussbericht des Programmträgers zum BLK-Programm

Der Abschlussbericht stellt den (vorläufigen) Endpunkt intensiver Forschungen zum Thema "Lernortkooperation" dar. Im Zeitraum von Oktober 1999 bis Dezember 2003 wurden 28 Modellversuche, die zum Thema Lernortkooperation arbeiteten, im Programm KOLIBRI ("Kooperation der Lernorte in der beruflichen Bildung") zusammengefasst. Die einzelnen Forschungsvorhaben untersuchten die verschiedenen Facetten von Lernortkooperation und konzipierten praktische Lösungen für die unterschiedlichsten Probleme. (DIPF/Orig.