Hasil untuk "Anthropology"

T. Asad

R. D'Andrade

G. Marcus, Michael M. J. Fischer

A. Kleinman, P. Benson

Cultural competency has become a fashionable term for clinicians and researchers. Yet no one can defi ne this term precisely enough to operationalize it in clinical training and best practices. It is clear that culture does matter in the clinic. Cultural factors are crucial to diagnosis, treatment, and care. They shape health-related beliefs, behaviours, and values. But the large claims about the value of cultural competence for the art of professional care-giving around the world are simply not supported by robust evaluation research showing that systematic attention to culture really improves clinical services. This lack of evidence is a failure of outcome research to take culture seriously enough to routinely assess the cost-effectiveness of culturally informed therapeutic practices, not a lack of effort to introduce ulturally informed strategies into clinical settings. [PLoS Medicine, October 2006]

M. Kearney

Marc Augé

H. Bernard

S. Mintz, C. Bois

J. Robbins

Loïc Wacquant

The anthropology of neoliberalism has become polarised between a hegemonic economic model anchored by variants of market rule and an insurgent approach fuelled by derivations of the Foucaultian notion of governmentality. Both conceptions obscure what is ‘neo’ about neoliberalism: the reengineering and redeployment of the state as the core agency that sets the rules and fabricates the subjectivities, social relations and collective representations suited to realising markets. Drawing on two decades of field-based inquiries into the structure, experience and political treatment of urban marginality in advanced society, I propose a via media between these two approaches that construes neoliberalism as an articulation of state, market and citizenship that harnesses the first to impose the stamp of the second onto the third. Bourdieu's concept of bureaucratic field offers a powerful tool for dissecting the revamping of the state as stratification and classification machine driving the neoliberal revolution from above and serves to put forth three theses: (1) neoliberalism is not an economic regime but a political project of state-crafting that puts disciplinary ‘workfare’, neutralising ‘prisonfare’ and the trope of individual responsibility at the service of commodification; (2) neoliberalism entails a rightward tilting of the space of bureaucratic agencies that define and distribute public goods and spawns a Centaur-state that practises liberalism at the top of the class structure and punitive paternalism at the bottom; (3) the growth and glorification of the penal wing of the state is an integral component of the neoliberal Leviathan, such that the police, courts and prison need to be brought into the political anthropology of neoliberal rule.

B. Latour

P. Harvey, H. Knox

Ryan V. Crawford, Jamie L. Crawford, Julie L. Hansen et al.

Abstract This study evaluated near‐infrared spectroscopy (NIRS) for nondestructive crude protein (CP) prediction in hemp (Cannabis sativa L.) grain and validated the biological basis of spectral predictions. Note that 149 whole grain samples from 38 cultivars were collected from New York trials (2017–2021) and validated for CP by combustion. Seven preprocessing methods were tested using 100 training/testing splits, with standard normal variate transformation following Savitzky–Golay filtering selected as optimal. Comparing algorithms showed that partial least squares regression (PLSR) significantly outperformed support vector machines and random forest. The best preprocessing method and algorithm was applied to 1000 additional splits. Optimal models contained 12 components with mean performance of root mean square error [RMSE] = 9.94, r2 = 0.84, relative predicted deviation [RPD] = 2.5, and ratio of performance to interquartile distance [RPIQ] = 3.94. More than 99% of the models had, at minimum, the ability to distinguish between high and low values, with 93.2% capable of quantitative prediction. To validate biological relevance, a protein‐focused model was developed using three known protein absorption bands (1200–1250, 1500–1550, and 2040–2090 nm). These models had substantially reduced performance with 86% of models capable of distinguishing between high and low values but only 14% of models capable of quantitative prediction. However, this targeted approach offers evidence that NIRS predictions are biologically grounded in protein‐specific spectral features rather than spurious correlations. This research demonstrates the promise and biological validity of NIRS for hemp grain CP assessment, supporting applications in breeding programs, although applications demanding more accurate prediction will require better models.

Alexander Sergeev, Valeriya Goloviznina, Mikhail Melnichenko et al.

Access to humanities research databases is often hindered by the limitations of traditional interaction formats, particularly in the methods of searching and response generation. This study introduces an LLM-based smart assistant designed to facilitate natural language communication with digital humanities data. The assistant, developed in a chatbot format, leverages the RAG approach and integrates state-of-the-art technologies such as hybrid search, automatic query generation, text-to-SQL filtering, semantic database search, and hyperlink insertion. To evaluate the effectiveness of the system, experiments were conducted to assess the response quality of various language models. The testing was based on the Prozhito digital archive, which contains diary entries from predominantly Russian-speaking individuals who lived in the 20th century. The chatbot is tailored to support anthropology and history researchers, as well as non-specialist users with an interest in the field, without requiring prior technical training. By enabling researchers to query complex databases with natural language, this tool aims to enhance accessibility and efficiency in humanities research. The study highlights the potential of Large Language Models to transform the way researchers and the public interact with digital archives, making them more intuitive and inclusive. Additional materials are presented in GitHub repository: https://github.com/alekosus/talking-to-data-intersys2025.

Omid Halimi Milani, Amanda Nikho, Marouane Tliba et al.

We introduce a novel deep learning framework for the automated staging of spheno-occipital synchondrosis (SOS) fusion, a critical diagnostic marker in both orthodontics and forensic anthropology. Our approach leverages a dual-model architecture wherein a teacher model, trained on manually cropped images, transfers its precise spatial understanding to a student model that operates on full, uncropped images. This knowledge distillation is facilitated by a newly formulated loss function that aligns spatial logits as well as incorporates gradient-based attention spatial mapping, ensuring that the student model internalizes the anatomically relevant features without relying on external cropping or YOLO-based segmentation. By leveraging expert-curated data and feedback at each step, our framework attains robust diagnostic accuracy, culminating in a clinically viable end-to-end pipeline. This streamlined approach obviates the need for additional pre-processing tools and accelerates deployment, thereby enhancing both the efficiency and consistency of skeletal maturation assessment in diverse clinical settings.

Zhou Ziheng, Huacong Tang, Mingjie Bi et al.

The evolution of morality presents a puzzle: natural selection should favor self-interest, yet humans developed moral systems promoting altruism. We address this question by introducing a novel Large Language Model (LLM)-based agent simulation framework modeling prehistoric hunter-gatherer societies. This platform is designed to probe diverse questions in social evolution, from survival advantages to inter-group dynamics. To investigate moral evolution, we designed agents with varying moral dispositions based on the Expanding Circle Theory \citep{singer1981expanding}. We evaluated their evolutionary success across a series of simulations and analyzed their decision-making in specially designed moral dilemmas. These experiments reveal how an agent's moral framework, in combination with its cognitive constraints, directly shapes its behavior and determines its evolutionary outcome. Crucially, the emergent patterns echo seminal theories from related domains of social science, providing external validation for the simulations. This work establishes LLM-based simulation as a powerful new paradigm to complement traditional research in evolutionary biology and anthropology, opening new avenues for investigating the complexities of moral and social evolution.

Egil Diau

The origins of economic behavior remain unresolved-not only in the social sciences but also in AI, where dominant theories often rely on predefined incentives or institutional assumptions. Contrary to the longstanding myth of barter as the foundation of exchange, converging evidence from early human societies suggests that reciprocity-not barter-was the foundational economic logic, enabling communities to sustain exchange and social cohesion long before formal markets emerged. Yet despite its centrality, reciprocity lacks a simulateable and cognitively grounded account. Here, we introduce a minimal behavioral framework based on three empirically supported cognitive primitives-individual recognition, reciprocal credence, and cost--return sensitivity-that enable agents to participate in and sustain reciprocal exchange, laying the foundation for scalable economic behavior. These mechanisms scaffold the emergence of cooperation, proto-economic exchange, and institutional structure from the bottom up. By bridging insights from primatology, developmental psychology, and economic anthropology, this framework offers a unified substrate for modeling trust, coordination, and economic behavior in both human and artificial systems. For an interactive visualization of the framework, see: https://egil158.github.io/cogfoundations-econ/

David M. Berry

If an active citizen should increasingly be a computationally enlightened one, replacing the autonomy of reason with the heteronomy of algorithms, then I argue in this article that we must begin teaching the principles of critiquing the computal through new notions of what we might call digital Bildung. Indeed, if civil society itself is mediated by computational systems and media, the public use of reason must also be complemented by skills for negotiating and using these computal forms to articulate such critique. Not only is there a need to raise the intellectual tone regarding computation and its related softwarization processes, but there is an urgent need to attend to the likely epistemic challenges from computation which, as presently constituted, tends towards justification through a philosophy of utility rather than through a philosophy of care for the territory of the intellect. We therefore need to develop an approach to this field that uses concepts and methods drawn from philosophy, politics, history, anthropology, sociology, media studies, computer science, and the humanities more generally, to try to understand these issues - particularly the way in which software and data increasingly penetrate our everyday life and the pressures and fissures that are created. We must, in other words, move to undertake a critical interdisciplinary research program to understand the way in which these systems are created, instantiated, and normatively engendered in both specific and general contexts.

Emily Corvi, Hannah Washington, Stefanie Reed et al.

Representational harms are widely recognized among fairness-related harms caused by generative language systems. However, their definitions are commonly under-specified. We make a theoretical contribution to the specification of representational harms by introducing a framework, grounded in speech act theory (Austin, 1962), that conceptualizes representational harms caused by generative language systems as the perlocutionary effects (i.e., real-world impacts) of particular types of illocutionary acts (i.e., system behaviors). Building on this argument and drawing on relevant literature from linguistic anthropology and sociolinguistics, we provide new definitions of stereotyping, demeaning, and erasure. We then use our framework to develop a granular taxonomy of illocutionary acts that cause representational harms, going beyond the high-level taxonomies presented in previous work. We also discuss the ways that our framework and taxonomy can support the development of valid measurement instruments. Finally, we demonstrate the utility of our framework and taxonomy via a case study that engages with recent conceptual debates about what constitutes a representational harm and how such harms should be measured.

Nitin Thombre, Pritesh Patil, Ankita Yadav et al.

Abstract The textile industry is one of the important and largest industry that consumes major chunk of the water in the world. This industry produces a large amount of wastewater during the processes such as sizing, de-sizing, scouring, bleaching, mercerizing, dyeing, printing, and finishing. The used water produced after such processes affects the environment heavily due to its composition such as mineral salts and oils present in suspended state, metals and metal complexes, dyes, various chemicals, some readily-biodegradable products and some constituents that are hard to biodegrade. The treatment of such hazardous effluent to reuse the water in certain water demanding processes is essential. Considering the worldwide application of the textiles, the appropriate management of water resources in the sector includes the treatment of effluent by efficient technology and the reuse of the water. This article displays an overview of waste management during textile industrial processes. It aims at giving oversight on waste minimization and reuse along with wastewater treatment methods. It also involves the cross-utilization of effluent between processes for achieving water efficiency. This review covers advanced waterless textile dyeing processes, zero liquid discharge techniques, advanced oxidation processes, biological treatment methods, which can be a sustainable and greener approach to reducing the waste generation.