Hasil untuk "Anthropology"

Paul Farmer

R. Behar

Victor Turner

T. Csordas

V. Smith

Jeffrey C. Johnson, D. Hruschka

The Handbook of Methods in Cultural Anthropology, now in its second edition, maintains a strong benchmark for understanding the scope of contemporary anthropological field methods. Avoiding divisive debates over science and humanism, the contributors draw upon both traditions to explore fieldwork in practice. The second edition also reflects major developments of the past decade, including: the rising prominence of mixed methods, the emergence of new technologies, and evolving views on ethnographic writing. Spanning the chain of research, from designing a project through methods of data collection and interpretive analysis, the Handbook features new chapters on ethnography of online communities, social survey research, and network and geospatial analysis. Considered discussion of ethics, epistemology, and the presentation of research results to diverse audiences round out the volume. The result is an essential guide for all scholars, professionals, and advanced students who employ fieldwork.

Dingyi Yang, Junqi Zhao, Xue Li et al.

Cognitive anthropology suggests that the distinction of human intelligence lies in the ability to infer other individuals' knowledge states and understand their intentions. In comparison, our closest animal relative, chimpanzees, lack the capacity to do so. With this paper, we aim to evaluate LLM performance in estimating other individuals' knowledge states and their potential actions. We design two tasks to test (1) if LLMs can predict story characters' next actions based on their own knowledge vs. improperly using information unavailable from their perspective, and (2) if LLMs can detect when story characters, through their actions, demonstrate knowledge they should not possess. Results reveal that most current state-of-the-art LLMs achieve near-random performance on both tasks, and are substantially inferior to humans. We argue future LLM research should place more weight on the abilities of knowledge estimation and intention understanding.

Jinhao Li, Zijian Chen, Tingzhu Chen et al.

Oracle bone inscriptions (OBIs) are the earliest known form of Chinese characters and serve as a valuable resource for research in anthropology and archaeology. However, most excavated fragments are severely degraded due to thousands of years of natural weathering, corrosion, and man-made destruction, making automatic OBI recognition extremely challenging. Previous methods either focus on pixel-level information or utilize vanilla transformers for glyph-based OBI denoising, which leads to tremendous computational overhead. Therefore, this paper proposes a fast attentive denoising framework for oracle bone inscriptions, i.e., OBIFormer. It leverages channel-wise self-attention, glyph extraction, and selective kernel feature fusion to reconstruct denoised images precisely while being computationally efficient. Our OBIFormer achieves state-of-the-art denoising performance for PSNR and SSIM metrics on synthetic and original OBI datasets. Furthermore, comprehensive experiments on a real oracle dataset demonstrate the great potential of our OBIFormer in assisting automatic OBI recognition. The code will be made available at https://github.com/LJHolyGround/OBIFormer.

Paulo Roger Lopes Alves, German Andres Estrada-Bonilla, Antonio Marcos Miranda Silva et al.

This study explored how different sugarcane vinasses influence the structure and composition of soil bacterial communities in two tropical Oxisols with contrasting textures. In a controlled microcosm experiment with sugarcane seedlings, two concentrations of three vinasse types were applied, and bacterial communities were monitored over 10, 30, and 60 days using T-RFLP and 16S rRNA gene sequencing. Across all treatments, vinasse application led to clear changes in bacterial community structure in both soils, regardless of the time point. Certain bacterial groups, such as <i>Sphingobacteriia</i>, <i>Alphaproteobacteria</i>, and <i>Gammaproteobacteria</i>, became more abundant—likely responding to increased carbon availability, higher pH, and greater soil moisture. At the same time, other groups declined, possibly due to excess nutrients like potassium and sulfur. Notably, these shifts occurred even when standard biochemical indicators suggested no major impact, highlighting the sensitivity of microbial community-level responses. These findings point to the importance of looking beyond traditional soil quality metrics when assessing the environmental effects of organic residue applications. Incorporating microbial indicators can offer a more nuanced understanding of how practices like vinasse reuse affect soil functioning in tropical agroecosystems.

Yuguo Jiang, Ziyu Zhao, Xinjie Zhao

This paper employs the super-efficiency slack-based measure (SBM) model with undesirable outputs to measure the green development efficiency of the agricultural product circulation industry (APCI) across 26 provinces of China from 2013 to 2022, and applies the kernel density estimation method to reveal its spatio-temporal evolution characteristics. Furthermore, the Tobit model is utilized to analyze the factors influencing the green development efficiency of the APCI. The research reveals that: (1)The green development efficiency of China's APCI is at a medium level. In 2019, a significant spatial demarcation emerged in the green development efficiency of China's agricultural product circulation industry, with the efficiency highland shifting from North/Northeast China to Southern regions, thereby manifesting a new ''high-south, low-north'' efficiency configuration. (2) Interprovincial disparities initially narrowed and subsequently widened. Furthermore, the six major regions exhibited heterogeneous dynamic characteristics, while the green development efficiency of the APCI demonstrated spatial imbalance across provinces. (3) The openness to international market (OIM) exerts a statistically significant positive effect on the green development efficiency of the APCI. Conversely, industrial structure (IS) and agricultural pollution level (API) demonstrate inhibitory effects on APCI's green development efficiency. This study deepens the understanding of APCI's green development efficiency, constructs a systematic measurement framework, expands research perspectives, and provides tools for governments, industries, and enterprises to evaluate efficiency accurately.

Handrian Ginting Jonson, Afrida Afrida, Zulkifli Addina et al.

This study examines the 2024 flash flood in Pandai Sikek, West Sumatra, through the lens of disaster anthropology and social memory. Based on preliminary research and one week of ethnographic fieldwork, the research reveals that while extreme rainfall triggered the event, socio-ecological drivers such as post-COVID return migration, deforestation, and land-use change significantly amplified its impacts. The community’s vulnerability was heightened by the absence of social memory: no oral traditions, rituals, or institutional practices existed to anticipate or respond to such a disaster. The flood therefore collapsed long-standing narratives of safety associated with Mount Singgalang and forced the community to confront a new reality of risk. Findings show that the disaster produced both trauma and solidarity, as gotong royong, remittances from migrants, and local organizing supported immediate recovery. At the same time, new and contested memories of vulnerability began to emerge. Early mitigation efforts, including reforestation, canal reinforcement, and disaster awareness initiatives, indicate steps toward resilience, though challenges remain in institutionalizing these lessons. The study concludes that building resilience in Pandai Sikek requires not only ecological restoration but also the transformation of traumatic absence into enduring social memory.

Riley C. W. O'Neill, Katrina Yezzi-Woodley, Jeff Calder et al.

Modern archaeological methods increasingly utilize 3D virtual representations of objects, computationally intensive analyses, high resolution scanning, large datasets, and machine learning. With higher resolution scans, challenges surrounding computational power, memory, and file storage quickly arise. Processing and analyzing high resolution scans often requires memory-intensive workflows, which are infeasible for most computers and increasingly necessitate the use of super-computers or innovative methods for processing on standard computers. Here we introduce a novel protocol for en-masse micro-CT scanning of small objects with a {\em mostly-automated} processing workflow that functions in memory-limited settings. We scanned 1,112 animal bone fragments using just 10 micro-CT scans, which were post-processed into individual PLY files. Notably, our methods can be applied to any object (with discernible density from the packaging material) making this method applicable to a variety of inquiries and fields including paleontology, geology, electrical engineering, and materials science. Further, our methods may immediately be adopted by scanning institutes to pool customer orders together and offer more affordable scanning. The work presented herein is part of a larger program facilitated by the international and multi-disciplinary research consortium known as Anthropological and Mathematical Analysis of Archaeological and Zooarchaeological Evidence (AMAAZE). AMAAZE unites experts in anthropology, mathematics, and computer science to develop new methods for mass-scale virtual archaeological research. Overall, our new scanning method and processing workflows lay the groundwork and set the standard for future mass-scale, high resolution scanning studies.

Liane Gabora

This chapter synthesizes evidence from cognitive science, evolutionary theory, anthropology, psychological studies, and computational models for a complex systems inspired theory of creativity, and its role in cultural evolution. Creativity is guided by the global shape of one's integrated network of memories, concepts, and beliefs: one's worldview. This integrated structure and its dynamical change over time are described using autocatalytic networks. Autocatalytic networks can interact with each other, and they can grow and evolve; through interactions between their components, they generate novel components. Thus, they are used to describe cultural change both within and between individuals, as well as across cultural lineages. The chapter outlines autocatalytic network models of the origin of culture, the cognitive developmental process by which each child becomes a participant in cultural evolution, and the role of imitation, leadership, and social media on cultural evolution, as well as the trade-off between creativity and continuity.

Kent K. Chang, Anna Ho, David Bamman

Television is often seen as a site for subcultural identification and subversive fantasy, including in queer cultures. How might we measure subversion, or the degree to which the depiction of social relationship between a dyad (e.g. two characters who are colleagues) deviates from its typical representation on TV? To explore this question, we introduce the task of stereotypic relationship extraction. Built on cognitive stylistics, linguistic anthropology, and dialogue relation extraction, in this paper, we attempt to model the cognitive process of stereotyping TV characters in dialogic interactions. Given a dyad, we want to predict: what social relationship do the speakers exhibit through their words? Subversion is then characterized by the discrepancy between the distribution of the model's predictions and the ground truth labels. To demonstrate the usefulness of this task and gesture at a methodological intervention, we enclose four case studies to characterize the representation of queer relationalities in the Big Bang Theory, Frasier, and Gilmore Girls, as we explore the suspicious and reparative modes of reading with our computational methods.

Lorenzo Pedroni, Daniel Zocchi Doherty, Chiara Dall’Asta et al.

Mycotoxins are known environmental pollutants that may contaminate food and feed chains. Some mycotoxins are regulated in many countries to limit the trading of contaminated and harmful commodities. However, the so-called emerging mycotoxins are poorly understood and need to be investigated further. Fusaric acid is an emerging mycotoxin, noxious to plants and animals, but is known to be less toxic to plants when hydroxylated. The detoxification routes effective in animals have not been elucidated yet. In this context, this study integrated in silico and in vitro techniques to discover potential bioremediation routes to turn fusaric acid to its less toxic metabolites. The toxicodynamics of these forms in humans have also been addressed. An in silico screening process, followed by molecular docking and dynamics studies, identified CYP199A4 from the bacterium Rhodopseudomonas palustris HaA2 as a potential fusaric acid biotransforming enzyme. Its activity was confirmed in vitro. However, the effect of hydroxylation seemed to have a limited impact on the modelled toxicodynamics against human targets. This study represents a starting point to develop a hybrid in silico/in vitro pipeline to find bioremediation agents for other food, feed and environmental contaminants.

Changui Lee, Seojeong Lee

The maritime sector is increasingly integrating Information and Communication Technology (ICT) and Artificial Intelligence (AI) technologies to enhance safety, environmental protection, and operational efficiency. With the introduction of the MASS Code by the International Maritime Organization (IMO), which regulates Maritime Autonomous Surface Ships (MASS), ensuring the safety of AI-integrated systems on these vessels has become critical. To achieve safe navigation, it is essential to identify potential risks during the system planning stage and design systems that can effectively address these risks. This paper proposes RA4MAIS (Risk Assessment for Maritime Artificial Intelligence Safety), a risk identification method specifically useful for developing AI-integrated maritime systems. RA4MAIS employs a systematic approach to uncover potential risks by considering internal system failures, human interactions, environmental conditions, AI-specific characteristics, and data quality issues. The method provides structured guidance to identify unknown risk situations and supports the development of safety requirements that guide system design and implementation. A case study on an Electronic Chart Display and Information System (ECDIS) with an AI-integrated collision avoidance function demonstrates the applicability of RA4MAIS, highlighting its effectiveness in identifying specific risks related to AI performance and reliability. The proposed method offers a foundational step towards enhancing the safety of software systems, contributing to the safe operation of autonomous ships.

Xinhui Zheng, Qiyan Tian, Qifeng Zhang

Recently, as humans have become increasingly interested in ocean resources, underwater vehicle-manipulator systems (UVMSs) have played an increasingly important role in ocean exploitation. To realize precise operation in underwater narrow spaces, the fly arm underwater vehicle manipulator system (FAUVMS) is proposed with manipulators as its core. However, this system suffers severe dynamic coupling effects due to the combination of small vehicle and big manipulators. To resolve this issue, we propose a robust adaptive controller that contains two parts. In the first part, the extended Kalman filter (EKF) is designed to estimate the system states and predicts external disturbances to achieve adaptive control. In the second part, a chattering-free sliding mode control (SMC) is designed to converge the tracking errors to zero, thus guaranteeing the robustness of the controller. We constructed the simulation platform based on the geometric model of FAUVMS, and various simulations are carried out under different situations. Compared to the traditional methods, the proposed method has a faster convergent speed, a better robustness and adaptiveness to external disturbances, and the tracking errors of positions of the vehicle and each end-effector are much smaller.

T. Eriksen

Tom Boellstorff

J. Allen, R. Jobson