Nourishing kai supports behaviour and concentration, tamariki learn well when food secure and eat regularly(1). Early food experiences influence our relationship with food as adults(2) and that tamariki health and wellbeing are shaped by education environments(3). WAVE (Well-being and Vitality in Education) has enduring partnerships with all preschools, kindergartens, playcentres, primary and secondary schools in our South Canterbury rohe(3), supporting healthy education environments with the goal of reducing inequities in health and education outcomes. Despite concerns about food security and processed foods, health promotion advisors note kaiako reluctance to promote nutrition using a whole-setting approach. The whole school approach(4) includes policies and procedures for kai (food) and wai (water), nutrition education within teaching and learning and nutrition messages promoted to whānau through enrolment information, learning stories/newsletters and displays, and in conversations with whānau. We describe an increase in kaiako acceptability occurring with the move from discussing nutrition as ‘healthy eating’ to using language of ‘supporting positive kai environments’. We include examples of mahi that the education settings put in place in this process. Between October 2023 and June 2024, WAVE provided internal professional development for health promotion kaimahi, focusing on supporting positive kai environments. Resources were redeveloped to align with messages about fostering positive relationships with kai and encouraging tamariki to be food explorers(5). The updated approach was widely communicated through newsletters and meetings with kaiako, alongside sharing relevant webinar and article resources from the Education Hub and Heart Foundation to support kaiako professional development. Health promotion advisors working with early childhood education and primary schools discussed nutrition within the broader context of positive kai and wai environments, aiming to develop positive relationships with food. These discussions took place through a combination of one-on-one meetings with lead kaiako each term and staff team meetings. Interview questions were sent to priority education (n=10) settings in September 2024 to gather feedback on barriers to promoting nutrition, how the change to ‘positive kai and supporting kai explorers’ has made a difference, and to hear the settings’ plans for current and future action in their setting. Responses from 8 ECE indicated that WAVE PD workshops using Heart Foundation resources were the resources they found most useful in enabling them to support tamariki as kai explorers. The shift to ‘positive kai environments’ has given kaiako consistent positive language around food, created space for tamariki to be self-directing with food, and has been mana-enhancing for tamariki and whānau. Kaiako stated that this evidence-based approach has taken the pressure off food, and kaiako are more responsive to tamariki needs. Kaiako are more willing to approach nutrition messages in a holistic manner to support tamariki.

In many countries, overall animal experimentation is not significantly decreasing or becoming less severe. Does this show that these countries’ programs to promote alternatives and the “three Rs” of “replace, reduce, refine” are failing? Scholars and activists sometimes take this for granted, but representatives of “three Rs” programs have disagreed. This article makes two contributions to the debate: one conceptual and one normative. First, it draws attention to the distinction between evaluating impact (whether a program makes a difference) and evaluating sufficiency (whether a program makes enough of a difference to achieve its goals). Total numbers are typically unhelpful in assessing impact, but depending on goals, they can be relevant in assessing sufficiency. Second, this article argues that an overall decrease in harm to animals in experimentation is a sensible policy goal. This article concludes with suggestions for how to go beyond the “three Rs” to effect overall change.

In this study, we perform a comprehensive evaluation of sentiment classification for German language data using three different approaches: (1) dictionary-based methods, (2) fine-tuned transformer models such as BERT and XLM-T and (3) various large language models (LLMs) with zero-shot capabilities, including natural language inference models, Siamese models and dialog-based models. The evaluation considers a variety of German language datasets, including contemporary social media texts, product reviews and humanities datasets. Our results confirm that dictionary-based methods, while computationally efficient and interpretable, fall short in classification accuracy. Fine-tuned models offer strong performance, but require significant training data and computational resources. LLMs with zero-shot capabilities, particularly dialog-based models, demonstrate competitive performance, often rivaling fine-tuned models, while eliminating the need for task-specific training. However, challenges remain regarding non-determinism, prompt sensitivity and the high resource requirements of large LLMs. The results suggest that for sentiment analysis in the computational humanities, where non-English and historical language data are common, LLM-based zero-shot classification is a viable alternative to fine-tuned models and dictionaries. Nevertheless, model selection remains highly context-dependent, requiring careful consideration of trade-offs between accuracy, resource efficiency and transparency.

Human societies reliably develop complex cultural traditions with striking similarities. These “super-attractors” span the domains of magic and religion (e.g., shamanism, supernatural punishment beliefs), aesthetics (e.g., heroic tales, dance songs), and social institutions (e.g., justice, corporate groups), and collectively constitute what I call the “cultural manifold.” The cultural manifold represents a set of equilibrium states of social and cultural evolution: hypothetically cultureless humans placed in a novel and empty habitat will eventually produce most or all of these complex traditions. Although the study of the super-attractors has been characterized by explanatory pluralism, particularly an emphasis on processes that favor individual- or group-level benefits, I here argue that their development is primarily underlain by a process I call “subjective selection,” or the production and selective retention of variants that are evaluated as instrumentally useful for satisfying goals. Humans around the world are motivated towards similar ends, such as healing illness, explaining misfortune, calming infants, and inducing others to cooperate. As we shape, tweak, and preferentially adopt culture that appears most effective for achieving these ends, we drive the convergence of complex traditions worldwide. The predictable development of the cultural manifold reflects the capacity of humans to sculpt traditions that apparently provide them with what they want, attesting to the importance of subjective selection in shaping human culture.

This special issue focuses on the many roles that context plays in neural machine translation and the evaluation of machine translation. It includes a survey of the current state of research related to context in machine translation and machine translation evaluation, along with three papers that focus on a variety of topics related to evaluation. Together, these highlight the wide range of topics in this area, and its increasing relevance as high-quality machine translation becomes increasingly common in a variety of settings.

To improve its management capacity, Frontiers Clinical and Translational Science Institute overhauled its evaluation infrastructure to be comprehensive, efficient, and transparent in demonstrating outputs and outcomes. We built a platform that standardized measures across program areas, integrated continuous improvement processes, and reduced the data entry burden for investigators. Using the Utilization-Focused Evaluation Framework, we created logic models to identify appropriate metrics. We built the evaluation data platform within REDCap to capture requests, events, attendance, and outcomes and to push work processes to Navigators. We initiated a membership model to serve as the backbone of the platform which allowed tailored communication, demographic data capture, and reduced data entry burden. The platform consists of nine REDCap projects across multiple programmatic areas. Using REDCap Dynamic SQL query fields and External Modules, the membership module was integrated into all forms to check and collect membership before service access. Data is synched to a dashboard for tracking outputs and outcomes in real-time. Since the launch of the evaluation platform in Fall 2022, Frontiers has increased its workflow efficiency and streamlined continuous improvement communication. The platform can serve as a model for other hubs to build efficient processes to create comprehensive and transparent evaluation plans.

The ARLE GPS tool provides computer-aided design support for solving problems with the spatial planning and design of houses, using a robust design model with physical-biological and cost strategies. This enables architects to eliminate uncertainties and to make robust decisions by applying computational thinking to decision making and action implementation. This support enables the architect to deal with the complexity arising from the interrelationships between the design variables and transforms the spatial planning problem, which is conceptualized as illdefined, into a well-defined problem. A scientific method is used, based on mathematical modeling of the action-decision field of design geometric variables, rather than a drawn method involving sketches. This tool acts as an aid mechanism, an assembler, a simulator, and an evaluator of geometric prototypes (virtual or graphical) and can be used to systematize the assembly or modeling of the FPL structure, particularly with respect to the performance required of a house. This candidate solution, provided by the tool, defines the spatial dimensions of the rooms in the house, the topological data of the assembly sequence, and the connections between rooms. The architect converts this virtual prototype into a graphical FPL prototype, which is then modeled, refined and evaluated continuously and objectively with the aid of ARLE GPS until a solution is obtained that satisfies the requirements, constraints and objectives of the problem. In this way, a solution to the problem (i.e., the project) can be captured and generated.