News media play a central role in shaping public perceptions of climate change, and whether coverage emphasizes threats or solutions affects audience engagement and policy support. We compare fine-tuned encoders and zero-shot prompting for classifying sentences from German-language climate news as threatoriented, solution-oriented, both, or neither, using two independent binary classifiers. We finetune three German encoders with context windows ranging from the target sentence alone to four preceding and four following sentences, and compare them with three open-weights generative models and the non-generative decision model Jev. All models are evaluated with article-grouped five-fold cross-validation on 440 manually coded Austrian newspaper articles. The best encoder, GBERT-large with the widest context, achieves weighted F1 scores of 0.805 (threat) and 0.802 (solution). The recently released MiMo-V2.6-Flash reaches 0.807 and 0.798 without task-specific training, placing both approaches on par, whereas earlier prompting models remain below the best encoder. Additional context improves all encoders consistently, by up to 4.8 F1 points. On the natural class distribution, MiMo-V2.6- Flash outperforms GBERT-large on threat classification, while GBERT-large retains a small advantage for solution classification. These results indicate that the gap between fine-tuned encoders and prompted language models has only recently been closed.

Bidirectional Topic Matching (BTM) is a novel method for cross-corpus topic modeling that quantifies thematic overlap and divergence between corpora. BTM is a flexible framework that can incorporate various topic modeling approaches, including BERTopic, Top2Vec, and Latent Dirichlet Allocation (LDA). It employs a dual-model approach, training separate topic models for each corpus and applying them reciprocally to enable comprehensive cross-corpus comparisons. This methodology facilitates the identification of shared themes and unique topics, providing nuanced insights into thematic relationships. A case study on climate news articles illustrates BTM’s utility by analyzing two distinct corpora: news coverage on climate change and articles focused on climate mitigation. The results reveal significant thematic overlaps and divergences, shedding light on how these two aspects of climate discourse are framed in the media.

This paper explores the automated extraction of job titles from unstructured historical job advertisements, using a corpus of digitized German-language newspapers from 1850-1950. The study addresses the challenges of working with unstructured, OCR-processed historical data, contrasting with contemporary approaches that often use structured, digitally-born datasets when dealing with this text type. We compare four extraction methods: a dictionary-based approach, a rule-based approach, a named entity recognition (NER) mode, and a text-generation method. The NER approach, trained on manually annotated data, achieved the highest F1 score (0.944 using transformers model trained on GPU, 0.884 model trained on CPU), demonstrating its flexibility and ability to correctly identify job titles. The text-generation approach performs similarly (0.920). However, the rule-based (0.69) and dictionary-based (0.632) methods reach relatively high F1 Scores as well, while offering the advantage of not requiring extensive labeling of training data. The results highlight the complexities of extracting meaningful job titles from historical texts, with implications for further research into labor market trends and occupational history.