1,720,985 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
The value of children: alloparenting in Samoa
Full text linkThis thesis examines the impact of alloparenting by children in Samoa. Survey data was used to explore whether children’s help in the household (“alloparental care”) influenced female fertility. I showed that children’s help had positive effects on both number of offspring and interbirth interval, but there was no influence of the sex of the first-born offspring; that is, having first-born daughters as potential helpers did not boost female reproduction compared to first-born sons. Building on this finding, I present ethological data on daily activities (including allocare) observed in twenty-five Samoan households in a single village. Contrary to received wisdom, these data showed that a division of labour by sex is not evident in children under the age of fifteen. I suggest this explains the lack of an effect of offspring sex. Finally, understanding the necessity of ecologically valid measures is explored through a series of open-ended interviews with Samoan women.International Society of Human Ethology (ISHE);
Wenner Gren Foundatio
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Assessing Surface Water Dynamics of Young Boreal Wetlands with Satellite Based Remote Sensing
No full textWetland landscapes are important ecological environments that provide many valuable ecological and socio-economic functions including water management and filtration, habitat provision for many plants and ani-mals, sources for tourism and recreation, and sources for peat and timber for industrial applications. Wet-lands are also under threat from factors such as climate change, industrial activity and development, and urban development. Many wetland restoration projects are underway to reclaim and restore damaged land-scapes to their previous state, but methods to evaluate the success of these reclamation activities need to be developed. Additionally, there is the need to compare and contrast these reclaimed landscapes with natural wetlands. In this thesis we present a comprehensive framework for evaluating and measuring water and vegetation dynamics in young wetlands using satellite based remote sensing. We use a combination of optical and radar imagery, and we approximate imagery obscured by clouds or shadows where appro-priate to build a complete picture of the landscape. We developed software tools to perform classification, visualize the results, and approximate missing data. We focused our measurement on open water areas, semi-aquatic emergent vegetation, and upland vegetation in areas surrounding the wetlands. We create easy to understand summary statistical images that can be used to measure the rate of occurrence of different phenomena or the variability of the phenomena. We also compare and contrast our approach with existing, established techniques for monitoring and measurement of Boreal wetlands. We show how our approach is able to improve on the results generated from these existing techniques. In some cases we are able to detect and measure wetlands that other techniques miss, while in other cases we are able to provide additional temporal context that other techniques might be missing
Federated Pseudo-labeling: A Data-Centric, Privacy-Preserving Framework for Medical Image Segmentation
No full textMedical image segmentation is essential for clinical diagnosis, treatment planning, and dis-ease monitoring. However, its advancement is impeded by strict data privacy regulations, the high cost of expert annotations, and significant variation in imaging protocols across institutions. These factors restrict the generation of large, centralized annotated datasets, limiting the generalizability of traditional deep learning models. Federated Learning (FL) has emerged as a decentralized alternative, enabling model training without data sharing. However, existing FL methods often rely on iterative parameter sharing and require uniform model architectures, which limit flexibility between institutions with diverse computational infrastructures and datasets. Parameter share also introduces residual privacy risks and significant communication overhead. Performance of FL degrades under non-identically dis-tributed data, a common characteristic in medical imaging. To address these challenges, we propose DCFed, a novel data-centric, semi-supervised FL framework that eliminates parameter sharing entirely. DCFed leverages federated pseudo-labeling on publicly available unlabeled datasets, using logit-based aggregation and uncer-tainty estimation. Clients independently train on their private datasets while collaboratively generating and refining pseudo-labels on a shared public dataset. These pseudo-labels are redistributed to improve local models without transmitting any weights or exposing sensi-tive data. Each client utilizes a customized U-Net backbone, enhanced with residual blocks, atrous spatial pyramid pooling for multi-scale feature extraction, and convolutional block attention modules to refine spatial and channel-wise representations. We have evaluated DCFed on two clinically relevant tasks: breast cancer segmentation from ultrasound images and skin lesion segmentation from dermoscopic images and observe performance improvements of up to 8.9% and 3.7%, respectively, over strong local baselines. Compared to standard FL, DCFed reduces communication overhead by more than 215× while achieving higher segmentation accuracy than both centralized and conventional FL approaches. Moreover, the performance of the proposed approach has been compared with standard FL methods such as FedAvg, FedNova, FedOpt and FedProx and DCFed outper-forms all these methods across most of the clients. Notably, DCFed is scalable across diverse client model architectures, accommodates clients with varying data volumes and labeling availability, and supports flexible collaboration settings, making it a highly practical and privacy-preserving solution for real-world medical image segmentation
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