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This study explores the visual representation of Great Yarmouth, a British coastal town caught between the urban and the rural, as seen through the quasi-monopolistic image search engine Google Images. The research examines levels of pluralistic or biased place representations to consider how rankings employed by Google Images algorithms represent Great Yarmouth’s identity. The study adopts a visual culture perspective that recognises the role of images in place making and combines digital methods with an image type analysis to investigate how online representations reflect and create the town’s identities. The data shows that Google Images’ preference for representing Yarmouth as a sunny seaside town indicates that the search engine prioritises marketable assets above its connections with its hinterland, its diversity of people, and the cultural activities it has to offer. This, the authors state, is a place far away from Tuan’s (1979) idea of a place that is given meaning and identity from the perspective of people. Instead, Google Images’ representations of Great Yarmouth are an example of a created form of place making as commodification. The article concludes that the inscribed bias and unbalanced search priority criteria employed by the search engine impact upon the diversity of the semi-peripheral town.

The text considers the Colombian armed conflict as a visual field made up of photographic images. The notion of “field” is assumed in a double sense: as the practice that gives rise to photographs and as the delimitation of a visible space and the construction of visibility. Therefore, we examine a photographic archive configured with material from three sources: Google images, reproductions of the press from various moments in the 1950s and 1980s, and material provided by victims of the armed conflict and by the Centro Nacional de Memoria Histórica. The reflection is oriented to think that the production and social circulation of photographs during the armed conflict can be seen as a “distribution of the sensible”. A partition in which an aesthetic is translated into an image policy that has determined which photographs (and which have not) deserved to be integrated into a hegemonic visual regime, and which image producers have been (and which have not been) part of that regime. [ABSTRACT FROM AUTHOR]

This study explores the visual representation of Great Yarmouth, a British coastal town caught between the urban and the rural, as seen through the quasi-monopolistic image search engine Google Images. The research examines levels of pluralistic or biased place representations to consider how rankings employed by Google Images algorithms represent Great Yarmouth's identity. The study adopts a visual culture perspective that recognises the role of images in place making and combines digital methods with an image type analysis to investigate how online representations reflect and create the town's identities. The data shows that Google Images' preference for representing Yarmouth as a sunny seaside town indicates that the search engine prioritises marketable assets above its connections with its hinterland, its diversity of people, and the cultural activities it has to offer. This, the authors state, is a place far away from Tuan's (1979) idea of a place that is given meaning and identity from the perspective of people. Instead, Google Images' representations of Great Yarmouth are an example of a created form of place making as commodification. The article concludes that the inscribed bias and unbalanced search priority criteria employed by the search engine impact upon the diversity of the semi-peripheral town. [ABSTRACT FROM AUTHOR]

This article examines the representations of Saudi women driving that circulated shortly after the lifting of the ban and considers the social, commercial and technological forces that helped to shape those representations. A corpus of images was collected from two international image banks – Getty and Shutterstock – as well as from a Google Image search. The images use Van Leeuwen's (2008) visual representation framework in Discourse and Practice: New Tools for Critical Analysis, paying particular attention to the similarities and differences between the images available in the image banks and those that were made prominent in the Google search. In addition, semantic metadata accompanying these images were also analysed in order to understand the linguistic constraints that had been put on searches for these images and the ontologies of the issue that they promoted. Finally, a more detailed analysis was performed on images that had been appropriated into different contexts such as news stories and advertisements to investigate how these images were adapted to support different political, cultural and commercial agendas. Findings suggest that images of Saudi women that circulated online internationally shortly after the lifting of the ban were mostly generic and decontextualized, creating simplified and trivialized depictions of gender relations and social change in the Kingdom. The analysis shows how commercial concerns which influence both the creation of stock images and the way they are taken up by news organizations and advertisers can sometimes have the effect of erasing the complexity of political events and reinforcing the very stereotypes they seem to be challenging. [ABSTRACT FROM AUTHOR]

Sustainable offtake of any threatened species and objective monitoring thereof relies on data‐driven and well‐managed harvest quotas and permit compliance. We used web‐sourced images of African leopard (Panthera pardus pardus) trophy hunts to determine whether online photographs could assist in monitoring and documenting trophy hunting in Africa. Of 10,000 images examined, 808 (8%) showed leopard trophy hunts and could be contextualized by date and country. From a subset of photos (n = 530), across six countries between 2011 and 2020, we extracted information on the leopards killed and hunter demographics. We found no significant differences in leopard sex, age, or shot wound position between countries, and most trophy leopards were in good physical condition. Most hunters were White (96%) and estimated at over 40 years old (82%), with the proportion of women hunters in younger age classes significantly higher than in older classes. Rifles, bows, and hounds were used in all countries, except Tanzania and Zambia, where rifles were exclusively used. Online images could not be reasonably compared to the CITES trade database, but in South Africa, more than half (57%) of all nationally registered leopard trophy hunts in the last decade (2010–2020) have been posted online. Online images also reveal hunting violations, including non‐permitted hunting of female leopards and illegal hounding. Such monitoring methods may become increasingly useful as social media usage grows and provide valuable insight into this multi‐million dollar industry. [ABSTRACT FROM AUTHOR]

Copyright of Revista General de Información y Documentación is the property of Universidad Complutense de Madrid and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Inclusive education is a global rights-based response to educational exclusion. It is communicated through a range of modalities, but analysis predominantly focuses on discourses constituted in written texts. Systematic research is needed to understand the discursive ensembles constituted by the visual mode. Our interest is in images of inclusive education that are available online and sourced via a Google images search. Using a critical approach to visual analysis, we conducted a visual content analysis and a multimodal discourse analysis. Four prominent discourses of inclusive education are evident: discourses of diversity, childhood, connection, and celebration. Education is a discursive absence. Each discourse reflects ideas of the field, but together these images present a distorted view of inclusive education and trivialise its concerns. This raises questions about the extent to which images can adequately capture the complexity and import of notions of access, equity and social justice. [ABSTRACT FROM AUTHOR]

The knowledge of weaving pattern design has spread to almost all regions in Thailand since prehistoric. The ethnic pattern design may vary based on ideology, beliefs, and traditions of people in each region. Until this day, the original weaving pattern may change by the adaptation of local textile designer and the Cross-cultural environment. Thus, the study of Thai weaving pattern design is necessary for reserving Thai ethnic wisdom of textile design. This work aims to develop the data dashboard by collecting Thai weaving pattern images from Google based on the information from the Royal Thai Silk Conservation Village Project, the Office of Sericulture Conservation and Standard Conformity Assessment, the Queen Sirikit Department of Sericulture, Ministry of Agriculture and Cooperatives. Our image gallery of Thai weaving patterns consists of 28 patterns from 4 regions: 1) The weaving patterns of northern Thailand include Jok Mae Jam, Jok Maueng Long, Yok Mook Lab Lae, Yok Dok Lamphun silk, and Sin Maueng Nan; 2) The weaving patterns of northeastern Thailand include Hol, Hang Kra Rok, Amprom, La Buek, Sin Tiew, Saked silk, Kaab Bua, Mud Mee Chonnabot silk, Luk Kaew patterned ebony-dyed silk, Mud Mee Teen Dang, Mud Mee Kaew Mookda silk, Mud Mee Kor Naree silk, Praewa silk, Mud Mee Soi Dok Mak silk, Samor, Anloonseem, and Kid; 3) The weaving patterns of central Thailand include Thai-Yuan Yok Mook and Thai-Yuan Jok; The weaving patterns of southern Thailand include Chuan Tani, Pum Riang silk, Yok Maueng Nakhon, and Na Muen Si. Our system can show the useful information of Thai weaving pattern such as the location on the map, the philosopher who gave the detail of weaving pattern, the weaving pattern design, and the textile techniques. [ABSTRACT FROM AUTHOR]

Theory on intraguild killing (IGK) is central to mammalian carnivore community ecology and top‐down ecosystem regulation. Yet, the cryptic nature of IGK hinders empirical evaluations. Using a novel data source – online photographs of interspecific aggression between African carnivores – we revisited existing predictions about the extent and drivers of IGK. Compared with seminal reviews, our constructed IGK network yielded 10 more species and nearly twice as many interactions. The extent of interactions increased 37% when considering intraguild aggression (direct attack) as a precursor of killing events. We show that IGK occurs over a wider range of body‐mass ratios than predicted by standing competition‐based views, with highly asymmetrical interactions being pervasive. Evidence that large species, particularly hypercarnivore felids, target sympatric carnivores with a wide range of body sizes suggests that current IGK theory is incomplete, underestimating alternative competition pathways and the role of predatory and incidental killing. Our findings reinforce the potential for IGK‐mediated cascades in species‐rich assemblages and community‐wide suppressive effects of large carnivores. [ABSTRACT FROM AUTHOR]

This study aims to explore the ability of Google Reverse Image Search (RIS) to detect plagiarism in images in the interior design field. Several image modifications were introduced by retaining the basic concept of the original image. These changes were classified into three categories as follow: a change in the design elements, introduced random changes by adding different objects to the existing image contents, and introduced various image effects. findings show that Google RIS does not take long to find newly uploaded images. Although it cannot detect changes related to the image contents, it can detect changes related to image size and contrast. Overall percentage of the modified images that were detected as matching the original image was only 5%. By contrast, the net percentage of images retrieved by Google RIS with contents actually related to the uploaded original image was 58.5%. Therefore, Google RIS is inaccurate in detecting any changes in the image contents irrespective of their simplicity, which implies that it cannot help in detection of visual plagiarism. [ABSTRACT FROM AUTHOR]

Birds with plumage colour aberrations are of interest to both the general public and scientists. However, due to their rarity in nature, information on the presence of colour aberrations is rarely found in the peer‐reviewed literature. Exploration of public observations using modern information technologies such as Internet‐based search engines could facilitate cost‐effective and rapid broad‐scale collection of data on phenotypic aberrations in animals but may also be prone to the same problems as fieldwork, including systematic collection bias. We used Google Images and also asked birdwatchers and ornithologists, via naturalists' forums and social media, to collate a unique dataset of photographs of 936 aberrantly coloured birds of 74 species from Poland. Phylogenetically informed analyses, which included species both with and without reported colour aberrations, revealed that the number of colour aberrations was higher in species with larger populations in Poland, those with larger body size, and those associated with human settlements. Colour aberrations were also more often reported for species with a wider habitat breadth and those which do not migrate over long distances. Habitat openness and diet type were not related to the number of colour aberrations across species in multivariate models. Our study emphasizes not only the power of novel sources to collect large datasets on relatively rare phenotypic aberrations in animals but also the importance of vigilance when using data mined from public sources because the observed patterns may reflect collection bias rather than the nature of the studied phenomena. [ABSTRACT FROM AUTHOR]

In a "colourblind" Europe, where race talk is taboo, explicit racial resentment towards newcomers is confined to the margins. Nonetheless, a racialized understanding of immigration and asylum persists, as evidenced in the less policed realm of iconographic representation. An analysis of the association between keyword-retrieved discursive frames and 1,500 photographs in Google Image search results from the years starting with the "migrant crisis" of 2015 reveals different regimes of representation and suggests that concepts of illegality and threat are embodied in images as race. Despite the overlapping hierarchies of origins found in today's racializing discourses, the pillars of old European racial taxonomies emerge as the prevalent codes of racialized difference in pictorial representation of a besieged Europe. [ABSTRACT FROM AUTHOR]

It is well established that the internet meme has come to represent a highly creative discursive device used to "facilitate the [...] communication of one's own political beliefs, attitudes and orientations" (Ross and Rivers 2017: 1). Although internet memes and political internet memes in particular have been addressed to many communicative situations such as participatory culture (e.g., Jenkins 2006; Shifman 2014; Theocharis 2015), one aspect that has not been paid enough attention to concerns the forms in which users refer to individual political figures and events in political memes. This being said, the present paper focuses on referring strategies (see Kirner-Ludwig and Zimmermann 2015; Kirner-Ludwig 2020) as employed in political internet memes on Reddit, including direct and indirect quotes, citations and allusions. A specific focus is going to be on such political internet memes that employ pop cultural and telecinematic reference points and recontextualize them from their original into new target contexts (see Bublitz 2015; Gruber 2019). As shall be shown, practices such as combining constructed speech elements into recontextualized elements in political internet memes create multiple intertextual references that may enhance visibility, saliency and, thus, the 'lifetime' of a political meme. [ABSTRACT FROM AUTHOR]

Searching for sources of inspiration is central to creative design; however, we have limited knowledge of individual inspiration search strategies in response to varying levels of task constraints. We studied 39 high-school students' inspiration search strategies using Google Images. Low task constrainedness led to divergent search marked by quick iterations, limited design task usage, and a heterogeneous image set. Intermediate constrainedness prompted in-depth, on-task exploration characterized by slow and careful iterations with more search result examination, extensive design task usage, and homogenous images. High constrainedness led to flexible bracketing with quick, flexible design task use, ending with heterogeneous images. Images from the intermediately and highly constrained conditions generated more ideas and were perceived as more inspiring (relative to low) in a new group of students. We discuss the idea of a 'sweet spot' of constrainedness in an inspiration search process in design and consider implications for design research and future work. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering & Sustainable Development is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The year 2020 brought a big concern for the global community because of COVID-19, which affected every sector of society, and tourism is no exception. Researchers across the globe are publishing their studies related to different dimensions of tourism in the context of COVID-19, and images have formed an essential component of their research. In tourism, images related to COVID-19 can open new dimensions for scholars. The main aim of the research is to measure the retrieval effectiveness of three image search engines (ISEs), that is, Bing Images, Google Images and Yahoo Image Search, concerning images related to COVID-19 and tourism. The study attempts to identify the capability of the ISEs to retrieve the desired and actual images related to COVID-19 and tourism. The PubMed Central (PMC) Database was consulted to retrieve the desired images and develop a testbed. The advanced search feature of PMC Database was explored by typing the search terms ‘COVID-19’ and ‘Tourism’ using ‘AND’ operator to make the search more comprehensive. Both the terms were searched against the ‘Figure/Table’ caption to retrieve papers carrying images related to COVID-19 and tourism. Queries were executed across the select ISEs, that is, Bing Images, Google Images and Yahoo Image Search. Retrieved images were individually analysed against the original image from the articles to determine the Precision, Relative Recall, F-Measure and Fallout Ratio. The format of the images in JPG/JPEG, besides checking the original image rank in the retrieved lot, was also ascertained. Bing Images scores more in terms of Mean Precision, followed by Google Images and Yahoo Image Search. For the Relative Recall measure, Google Images scores high, followed by Bing Images and Yahoo Image Search, respectively. Regarding F-Measure and Fallout Ratio, Bing Images outperforms Google Images and Yahoo Image Search. In retrieving the sought format of JPG/JPEG, Google Images performs best, followed by Yahoo Image Search and Bing Images. Google Images produces the original image at the first rank on more than one occasion. In contrast, Bing Images retrieves the original image at the first rank in two instances. Yahoo Images performs poorly over this metric as it does not retrieve any original image at the first rank on any other instance. The study cannot be generalised as the scope is only limited to the images indexed by PMC. Furthermore, the retrieval effectiveness of only three ISEs is measured. The study is the first to measure the retrieval effectiveness of ISEs in retrieving images related to the COVID-19 pandemic and tourism. The study can be extended across other image-indexing databases pertinent to tourism studies, and the retrieval effectiveness of other ISEs can also be considered. [ABSTRACT FROM AUTHOR]

Describing spatial patterns of phenotypic traits can be important for evolutionary and ecological studies. However, traditional approaches, such as fieldwork, can be time-consuming and expensive. Information technologies, such as Internet search engines, could facilitate the collection of these data. Google Images is one such technology that might offer an opportunity to rapidly collect information on spatial patterns of phenotypic traits., We investigated the use of Google Images in extracting data on geographical variation in phenotypic traits visible from photographs. We compared the distribution of visual traits obtained from Google Images with four previous studies: colour morphs of black bear ( Ursus americanus); colouration and spottiness in barn owl ( Tyto alba); colour morphs of black sparrowhawk ( Accipiter melanoleucus) and the distribution of hooded ( Corvus corone) and carrion crows ( Corvus cornix) across their European hybrid zone. Additionally, we develop and present a web application ( Morphic), which facilitates the human data capture process of this method., We found good agreement between fieldwork data and Google Images data across all studies. Indeed, there was strong agreement between the data obtained from the original study and from the Google Images method for the colour morphs of black bear ( R2 = 80%) and for two barn owl plumage traits ( R2 = 64% and 53%). Our approach also successfully matched the clinal variation of black sparrowhawks morphs across South Africa. Our method also gave a good agreement between the distribution of hooded and carrion crows (with 86% placed on the correct side of the hybrid zone line)., Our results suggest that this method can work well for visible traits of common and widespread species that are objective, binary, and easy to see irrespective of angle. The Google Images method is cost-effective and rapid and can be used with some confidence when investigating patterns of geographical variation, as well as a range of other applications. In many cases, it could therefore supplement or replace fieldwork. [ABSTRACT FROM AUTHOR]

Copyright of Revista Brasileira de Agricultura Irrigada - RBAI is the property of Revista Brasileira de Agricultura Irrigada - RBAI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Conventional object recognition techniques rely heavily on manually annotated image datasets to achieve good performances. However, collecting high quality datasets is really laborious. The image search engines such as Google Images seem to provide quantities of object images. Unfortunately, a large portion of the search images are irrelevant. In this paper, we propose a semi-supervised framework for learning visual categories from Google Images. We exploit a co-training algorithm, the CoBoost algorithm, and integrate it with two kinds of features, the 1st and 2nd order features, which define bag of words representation and spatial relationship between local features, respectively. We create two boosting classifiers based on the 1st and 2nd order features in the training, during which one classifier provides labels for the other. The 2nd order features are generated dynamically rather than extracted exhaustively to avoid high computation. An active learning technique is also introduced to further improve the performance. Experimental results show that the object models learned from Google Images by our method are competitive with the state-of-the-art unsupervised approaches and some supervised techniques on the standard benchmark datasets. [ABSTRACT FROM AUTHOR]

This research discusses the potential of using big data for vocabulary learning from the perspective of learner-generated pictorial annotations. Pictorial annotations lead to effective vocabulary learning, the creation of which is however challenging and time-consuming. As user-generated annotations promote active learning, and in the big data era, data sources in social media platforms are not only huge but also user-generated, the proposal of using social media data to establish a natural and semantic connection between pictorial annotations and words seems feasible. This research investigated learners' perceptions of creating pictorial annotations using Google images and social media images, learners' evaluation of the learner-generated pictorial annotations, and the effectiveness of Google pictorial annotations and social media pictorial annotations in promoting vocabulary learning. A total of 153 undergraduates participated in the research, some of whom created pictorial annotations using Google and social media data, some evaluated the annotations, and some learned the target words with the annotations. The results indicated positive attitudes towards using Google and social media data sets as resources for language enhancement, as well as significant effectiveness of learner-generated Google pictorial annotations and social media pictorial annotations in promoting both initial learning and retention of target words. Specifically, we found that (i) Google images were more appropriate and reliable for pictorial annotations creation, and therefore they achieved better outcomes when learning with the annotations created with Google images than images from social media, and (ii) the participants who created word lists that integrate pictorial annotations were likely to engage in active learning when they selected and organized the verbal and visual information of target words by themselves and actively integrated such information with their prior knowledge. [ABSTRACT FROM AUTHOR]