Blurred Boundaries Research Articles

Skin lesion image segmentation by using backchannel filling CNN and level sets

Pigmented skin lesion image segmentation is a key step in the computer-aided diagnosis of melanoma. This image segmentation task is also difficult due to blurred skin lesion boundaries, uneven color, and diverse textures. To improve the accuracy of skin lesion image segmentation, this paper proposes an image segmentation method that combines convolutional neural networks (CNN) and level sets. First, a backchannel filling (BCF) method is proposed. Through the first forward propagation of the CNN, the target position estimated by the CNN is backfilled into the position prior channel. Then, end-to-end deep learning models based on BCF multibranch encoding–decoding CNN (BCF-CNN) and level sets are built. The initial level set and driving energy are predicted through the second forward propagation of the BCF-CNN and then input into the level set segmentation model to complete the evolution. Additionally, a learning algorithm that combines a joint BCF-CNN and a level set is designed to train the segmentation model. The experimental results of the proposed algorithm are compared with those of other state-of-the-art algorithms on both the International Skin Imaging Collaboration (ISIC) 2017 and 2016 skin lesion challenge datasets, demonstrating that the proposed algorithm outperforms the others in terms of accuracy with a Jaccard index of approximately 77.0% and 85.1% on the ISIC 2017 and 2016 skin lesion challenge datasets, respectively.

POSSESSION AND CONSUMPTION: CLARICE LISPECTOR AND THE ETHICS OF MYSTICISM

Abstract Many critics have called Clarice Lispector a mystic. Lispector, however, was not a religious figure, but rather a 20th-century Brazilian writer who was influenced by both her Jewish background and her Catholic Brazilian context. There are various forms of Jewish and Christian mysticism that reject transcendent union with God and, by referencing them, I elucidate the complexity of Lispector’s mystical fiction. By looking at challenges to mystical union in these traditions, I aim to show the ethical complexity of this concept and how that complexity is deepened through Lispector’s writing as she problematises blurred boundaries between self and Other.

Debating Human-Nonhuman Boundaries in Philip K. Dick's Do Androids Dream of Electric Sheep?

This paper explores the issue of blurred boundaries between human and nonhuman (machines) in a technologically advanced world of Philip K. Dick’s novel, Do Androids Dream of Electric Sheep? employing theoretical perspective of posthumanism. It critically examines how machines, in the form of humanoid robots, are endowed with qualities that are traditionally considered human qualities. This concept of humanoid robot is unique in terms of technical artifacts and human relationship to technology. The traditional idea of humanism that regards all other nonhumans as subordinate to humans cannot function in this case. The theoretical parameters like natural and artificial, mind and body, self-developing and externally designed are used to see whether the boundary between human and machines is blurred or not. This study exposes that the writer artistically blurs the boundaries between humans and nonhumans by presenting highly advanced humanoid robots called androids with a complex Nexus-6 operating system that functions as both subject and object, user and used. Then, it moves to show the need of the present time, that is the companion relationship between man and machines. Such change gives new ways of dealing with modern problems caused by the technological advancement. Following qualitative research design and using critical analytical method for textual analysis, this study examines the dynamics of human-nonhuman relationship and blurred boundaries with the light of posthumanism. It uses the theoretical insights of Rosi Braidotti and Donna Harraway in the act of critical analysis of the textual data.

Robust multi-focus image fusion using focus property detection and deep image matting

Multi-focus image fusion techniques are designed to integrate the focused regions of multiple source images to produce an all-in-focus image. Unfortunately, the fusion results generated by existing methods are prone to suffer from chromatic aberrations and blurred boundary regions. In this paper, a multi-focus image fusion framework combining focus property detection and deep image matting is proposed to achieve high-quality fusion results. Specifically, we introduce spatial frequency detection blocks, which integrate the spatial frequency information of source images at different levels to achieve effective focus property detection for the initial decision. Then, the trimap acquired from the initial decision is optimized using a deep matting model so as to further detect the focused/defocused boundaries and obtain a more precise decision map. Meanwhile, a new hybrid loss function is introduced, and a large-scale dataset is created to improve the performance of the fusion framework. Finally, the fusion result is obtained by integrating the focused regions via the decision map. Compared to 18 other advanced fusion methods, the proposed method outperforms all of them in terms of the seven objective metrics evaluated on two publicly available datasets. These experimental results provide strong evidence that the proposed fusion framework excels not only in subjective vision but also in quantitative analysis. The code of the proposed method will be available at https://github.com/govenda/DIMF.

Boundary-guided and Region-aware Network with Global Scale-adaptive for Accurate Segmentation of Breast Tumors in Ultrasound Images.

Breast ultrasound (BUS) image segmentation is a critical procedure in the diagnosis and quantitative analysis of breast cancer. Most existing methods for BUS image segmentation do not effectively utilize the prior information extracted from the images. In addition, breast tumors have very blurred boundaries, various sizes and irregular shapes, and the images have a lot of noise. Thus, tumor segmentation remains a challenge. In this paper, we propose a BUS image segmentation method using a boundary-guided and region-aware network with global scale-adaptive (BGRA-GSA). Specifically, we first design a global scale-adaptive module (GSAM) to extract features of tumors of different sizes from multiple perspectives. GSAM encodes the features at the top of the network in both channel and spatial dimensions, which can effectively extract multi-scale context and provide global prior information. Moreover, we develop a boundary-guided module (BGM) for fully mining boundary information. BGM guides the decoder to learn the boundary context by explicitly enhancing the extracted boundary features. Simultaneously, we design a region-aware module (RAM) for realizing the cross-fusion of diverse layers of breast tumor diversity features, which can facilitate the network to improve the learning ability of contextual features of tumor regions. These modules enable our BGRA-GSA to capture and integrate rich global multi-scale context, multi-level fine-grained details, and semantic information to facilitate accurate breast tumor segmentation. Finally, the experimental results on three publicly available datasets show that our model achieves highly effective segmentation of breast tumors even with blurred boundaries, various sizes and shapes, and low contrast.

Soil CT image quality enhancement via an improved super-resolution reconstruction method based on GAN

Computed tomography (CT) is an effective instrument to characterize the internal structure of soil. However, the resolution of soil CT images is often limited by the physical properties of the scanner and the imaging protocol, which can lead to difficulties in accurately characterizing fine-scale features of soil. At present, the super-resolution reconstruction results of CT images often have the problems of blurred feature boundaries and low image quality, causing inaccuracies in the analysis of soil structure. Therefore, this study developed an improved super-resolution reconstruction method based on generative adversarial networks (SRLGAN) to accurately reconstruct high-resolution CT images from low-resolution CT images and assist in digital soil descriptions. SRLGAN utilized a lightweight CNN super-resolution reconstruction module as the generator model, which can improve the reconstruction image quality while reducing the number of trainable parameters and inference time. Meanwhile, a closed-loop loss function was employed to reduce input image information loss and improve method generalization. Compared with traditional reconstruction methods and deep learning methods, the proposed SRLGAN method showed superior performance with a higher peak signal-to-noise ratio (PSNR) of 45.869 dB and a structural similarity index (SSIM) of 0.992. Particularly, the PSNR was 9.95% and 4.49% higher than the best-performing traditional method (Bicubic) and deep learning method (SRGAN), respectively. Furthermore, compared to the SRGAN, trainable parameters and inference time of the SRLGAN have decreased by 77.84% and 57.86%, respectively, which indicates the highest degree of lightweighting. This study demonstrated that SRLGAN can generate high-quality, high-resolution soil CT images that can be used for subsequent segmentation and 3D structural analysis, offering an intelligent approach to understanding the internal soil structure.

Examining employee experiences of hybrid work: an ecological approach

PurposeThe purpose of this paper is to explore the various forms of organizational and team-level actions that were perceived to be helpful or not helpful by employees as they navigate the hybrid work arrangements and how these had an impact on their work behaviors and experiences. This research utilized Bronfenbrenner's ecological systems theory as framework.Design/methodology/approachThe exploratory study used a qualitative approach in gathering data via online survey from a total of 45 Filipino employees working in a hybrid work arrangement for at least three months. The analysis utilized both inductive and deductive methodologies in examining the data. Inductive thematic analysis was used in coding the data based on the participants' responses, while the deductive approach ensured that the themes are aligned with the research questions and reflect the different systems within Bronfenbrenner's EST (1986).FindingsResults surfaced helpful organizational (e.g. provision of work tools, financial assistance, supportive policies and engagement and wellness initiatives) and team level actions (i.e. use of technology-based communication tools, open virtual door policy, effective performance management system, employee care practices and team engagement activities). Actions that were perceived as not helpful include inadequate technological infrastructure, poor communication, insufficient training, punitive policies/practices and leadership issues at the organizational level as well as unresponsive colleagues and ineffective implementation of policies/processes at the level of teams. Employees reported being able to build on savings, becoming more productive and having greater work–life balance amid hybrid work. However, they continue to be challenged by blurred boundaries and inability to disconnect from work similar to when work was done remotely and now with sustaining momentum given the shifts on where they do their work.Practical implicationsThe findings of this study may guide programs and initiatives of human resource management practitioners and organizational leaders as they support employees in navigating through hybrid work.Originality/valueThe research expands extant knowledge on practices and experiences in hybrid work (Gifford, 2022). It also contributes to studies on human resource management that are nuanced based on where work is performed (Ng and Stanton, 2023) or with emerging work arrangements.

Diverse students' perspectives on privacy and technology integration in higher education

AbstractThe rapid increase and use of educational technologies have a significant impact on students' privacy, personal information and metadata in higher education. The past two decades have also witnessed the process where information about students is stored and handled outside premises and control of learning institutions. The personal information about students and their activity while they interact with learning management systems (LMS) and online learning tools is increasing and is stored in cloud computing platforms, software‐as‐a‐service providers and other vendors. There are reported cases where students' personal data and information have been leaked, with possible privacy violations. Although this issue affects all students, it is important to understand the perspectives of culturally diverse students on the balancing of personal data/information and the use of technology integration in higher education. The purpose of this research was to investigate diverse students' perspectives on privacy, control of personal information, confidentiality and technology integration. The study also aimed to examine the challenges and experiences of loss of control over personal data and metadata encroachment, and protection of privacy in online education. Data were collected following a qualitative research design. Forty in‐depth and semi‐structured focus‐group interviews (26 female and 24 male students) from four academic disciplines (Education, Social Sciences, Business, and Health Sciences) in a public university in the United States were obtained and analysed following a constant comparative analysis approach. Findings show evidence of fear and anxiety about data encroachment, challenges from bureaucratic procedures and lack of educational awareness, privacy concerns about LMS and social media. These results contribute to a new understanding regarding diverse students' knowledge, challenges and experiences on how to balance privacy, confidentiality and technology integration in higher education. Practitioner notesWhat is already known about this topic Considering the increasing Internet world, where new technologies and mobile computing are interwoven in the use of digital technologies and access to information, balancing students' privacy and technology integration is essential in providing students assurance of their data security and privacy. Few studies have explored diverse students' perspectives on privacy and technology integration that include control of personal information, confidentiality and technology integration. Further, there is a dearth of studies on the challenges and experiences of diverse students regarding the loss of control over personal data and metadata encroachment, and protection of privacy in online education. Although rapid digitalization sparked studies about privacy and data usage, such as the perception of privacy breaches for the purpose of COVID‐19 pandemic control, few studies have also raised privacy issues related to student data usage in higher education institutions (HEIs) in the post‐COVID‐19 context. To realise justice technology integration and data usage in HEIs, students' perspectives on privacy should be examined. What this paper adds The results show the following: fear and anxiety about data encroachment, challenges from bureaucratic procedures and lack of educational awareness, privacy concerns about LMS and social media. The result from this work also illustrates that it would be hard for students to evaluate their privacy risk due to lack of educational opportunity to balance privacy and technologies usage, and the blurred boundaries between LMS and third parties including social media in technology integration. Implications for practice and/or policy The paper identifies how the rapid increase and use of educational technologies have, in many ways, influenced diverse students' personal information and metadata in higher education. The paper makes detail recommendations on how HEIs can address the issue of balancing privacy and technology integration as an essential strategy by providing students assurance of their data security and privacy.

Corona virus disease 2019 lesion segmentation network based on an adaptive joint loss function

Corona virus disease 2019 (COVID-19) is an acute respiratory infectious disease with strong contagiousness, strong variability, and long incubation period. The probability of misdiagnosis and missed diagnosis can be significantly decreased with the use of automatic segmentation of COVID-19 lesions based on computed tomography images, which helps doctors in rapid diagnosis and precise treatment. This paper introduced the level set generalized Dice loss function (LGDL) in conjunction with the level set segmentation method based on COVID-19 lesion segmentation network and proposed a dual-path COVID-19 lesion segmentation network (Dual-SAUNet++) to address the pain points such as the complex symptoms of COVID-19 and the blurred boundaries that are challenging to segment. LGDL is an adaptive weight joint loss obtained by combining the generalized Dice loss of the mask path and the mean square error of the level set path. On the test set, the model achieved Dice similarity coefficient of (87.81 ± 10.86)%, intersection over union of (79.20 ± 14.58)%, sensitivity of (94.18 ± 13.56)%, specificity of (99.83 ± 0.43)% and Hausdorff distance of 18.29 ± 31.48 mm. Studies indicated that Dual-SAUNet++ has a great anti-noise capability and it can segment multi-scale lesions while simultaneously focusing on their area and border information. The method proposed in this paper assists doctors in judging the severity of COVID-19 infection by accurately segmenting the lesion, and provides a reliable basis for subsequent clinical treatment.

Translingualism and Histories of Violence in Francophone Algerian Writing

Abstract This article explores the complexity of translingualism in francophone Algerian writing and offers a close reading of Assia Djebar’s Vaste est la prison (1995) to demonstrate the variegated ways in which characters experience the interaction between languages in Algerian history. Drawing on thinkers such as Stefan Helgesson and Christina Kullberg, the article conceptualises ‘translingualism’ not so much as a sign of linguistic mastery, as has been the case in some earlier studies, but as a series of ‘events’ in which encounters between languages either blur or foreground boundaries in various ways at moments in texts where the history of Western imperialism and postcolonialism also play a role. This notion of events informs a reading of francophone Algerian writing, and in particular of Djebar, attentive to the subtle mingling and friction between languages as individuals use them in multiple and changing ways.

Translingualism and Histories of Violence in Francophone Algerian Writing

Abstract This article explores the complexity of translingualism in francophone Algerian writing and offers a close reading of Assia Djebar’s Vaste est la prison (1995) to demonstrate the variegated ways in which characters experience the interaction between languages in Algerian history. Drawing on thinkers such as Stefan Helgesson and Christina Kullberg, the article conceptualises ‘translingualism’ not so much as a sign of linguistic mastery, as has been the case in some earlier studies, but as a series of ‘events’ in which encounters between languages either blur or foreground boundaries in various ways at moments in texts where the history of Western imperialism and postcolonialism also play a role. This notion of events informs a reading of francophone Algerian writing, and in particular of Djebar, attentive to the subtle mingling and friction between languages as individuals use them in multiple and changing ways.

MCGNet: Multi-level consistency guided polyp segmentation

Polyp segmentation is challenging due to the varying shapes and sizes, and low contrast, resulting in blurred segmentation boundaries. To address this problem, we propose a multi-level consistency guided network (MCGNet), which performs joint supervision at three different levels: (i) at size level, we obtain two inputs with consistent content and different sizes by applying affine transformation to the input image; (ii) at boundary level, we utilize two modules, Multi-scale Attention (MA) and Feature Similarity Aggregation (FSA), to reinforce the boundary information and learn the boundary consistency in the output layer; (iii) at class activation map level, we follow a consistency regularization approach to restrict the range of class activation maps in the middle layer by Pixel Correlation Attention (PCA) module. Experimental results on 5 widely used datasets show that the MCGNet achieves state-of-the-art performance and exhibits outstanding generality.

Visualising “unhomeliness” and tracing trauma in Malik Sajad’s graphic memoir Munnu: A Boy from Kashmir

ABSTRACT The paper deals with the sudden experience of an unfamiliar feeling due to the blurring of the boundaries between the private and the public space, resulting in what Bhabha calls ‘unhomely.’ It has been further argued that this sudden emergence of unfamiliar experiences acts as a traumatic event, represented through graphic narration in Malik Sajad’s Munnu: A Boy from Kashmir. The sense of ‘unhomeliness’ caused due to various factors like constant surveillance is responsible for the traumatic feeling in Munnu. The paper also explores the idea of the panopticon, which engenders the blurring of the boundaries between the home and the world. The blurred boundaries make our private space strange and unfamiliar, which is supposed to be an intimate place without any invasion by the external world. Therefore, the present study explores the notion of ‘unhomely’ and the trauma caused by it, as represented in the graphic narration of the events through dreams and nightmares.

Stakeholder conflict and standard-setting foundation oversight

This study examines foundation oversight authority as a distinct tool used to maintain the accounting profession’s delegated authority to set standards. Prior literature traditionally focuses on the standard-setting boards and technical arguments surrounding proposed accounting standards. We examine whether the Financial Accounting Foundation (FAF) can manage stakeholder conflict and legitimize contentious activity through exercise of its oversight role. The presented case is a qualitative analysis of the FAF’s GASB Scope of Authority project, a rare public exercise of FAF oversight authority, in which the FAF applied its authority despite stakeholder disagreement on the line between oversight and standard setting. We observe the FAF’s oversight of standard setting due process as opposed to the content of proposed standard setting projects. The policy formalizes private consultation at this blurred boundary to safeguard board autonomy and delegated authority. Implications and precedence for the FASB and other contentious accounting issues (e.g., deliberations around ESG reporting) are discussed.

Up the Hill: The Familial-institutional Reproduction of the Black Upper-middle Class

This article explores the distinct social reproductive practices of the Black upper-middle class. In particular, this study focuses on the role of community organizations in socializing the collective habitus of a community at the intersection of Blackness and class privilege. I draw on interview data from members of one Black upper-middle class organization, Jack and Jill of America Inc., to identify how families and institutions collaborate to socialize children into a particular raced-classed habitus, passing on ideologies, discursive habits, and behavioral strategies aimed at producing citizens who are both culturally empathetic and socioeconomically mobile. I find that mothers rely on the blurred boundaries between family and social organizations to legitimate and reinforce the lessons taught at home. In particular, mothers explicitly socialize identity and affinity within a racial community while simultaneously socializing implicit, but powerful, behavioral habits related to social class.

“She’s Pretty in Her Pictures but in Real Life She’s Ugly”: School Pupils Negotiating the Blurred Boundaries between Online and Offline Social Contexts

Online social interaction has become integral to contemporary social life, adding new dimensions to how young people learn, interact, and perceive themselves and one another. We present findings from a yearlong ethnographic study within a Scottish state secondary school to explain pupils’ informal social relationships. We particularly investigate how school pupils experience social life inside and outside of school in relation to presenting themselves on social media and consider how they negotiate the overlap between their online (social media) representations and offline (school) encounters with their peers. Our findings evidence that pupils engaged in self-presentation within and across online and offline social contexts, whilst experiencing pressure to ‘keep up appearances’ between the two. The online environment afforded pupils greater control over self-presentation, especially in relation to bodily appearances. Here, pupils had time and tools to construct idealised fronts and to amass online capital. In some circumstances, this capital could have exchange value within offline environments. However, young people were at continual risk of having their carefully constructed identities discredited when in an in-person setting. We explore these issues in relation to pupil health, wellbeing, and learning, and we consider how educators may respond.

Consistent Learning-Based Breast Tumor Segmentation and Its Application in Sentinel Lymph Node Metastasis Prediction.

Accurate staging of lymph nodes provides crucial diagnostic information for breast cancer patients, where segmentation is of great importance by localizing and visualizing the breast tumor of interest. Nevertheless, current segmentation methods perform average when facing large span of tumor sizes, degraded image quality, blurred tumor boundaries, and resulting noise during manual annotation. Therefore, we develop a Multi-scale RepVGG-based Segmentation Network (MPSegNet) to segment breast tumor from MR images. In particular, we construct a consistent learning framework for the MPSegNet to alleviate the impact of noisy labels upon segmentation results. The rationale is that different views covering the same breast tumors are supposed to generate identical segmentation predictions. Then, we predict SLN metastasis given segmented breast tumors, where we evaluate the relationships between the predictive performance and tumor segmentations under different consistencies. The results show the superiority of our method over other state-of-the-art methods. A high consistency among multiple views can boost the segmentation performance during consistent learning. However, the optimal segmentation does not produce the best SLN metastatic prediction results, implying that the dependence of classification upon segmentation needs to be elaborately investigated further.Clinical Relevance- This study facilitates more accurate segmentation of breast tumors with consistent learning, and provides an initial analysis between tumor segmentation and subsequent prediction of SLN metastasis, which has potential significance for the precise medical care of breast cancer patients.

Alignment Integration Network for Salient Object Detection and Its Application for Optical Remote Sensing Images.

Salient object detection has made substantial progress due to the exploitation of multi-level convolutional features. The key point is how to combine these convolutional features effectively and efficiently. Due to the step by step down-sampling operations in almost all CNNs, multi-level features usually have different scales. Methods based on fully convolutional networks directly apply bilinear up-sampling to low-resolution deep features and then combine them with high-resolution shallow features by addition or concatenation, which neglects the compatibility of features, resulting in misalignment problems. In this paper, to solve the problem, we propose an alignment integration network (ALNet), which aligns adjacent level features progressively to generate powerful combinations. To capture long-range dependencies for high-level integrated features as well as maintain high computational efficiency, a strip attention module (SAM) is introduced into the alignment integration procedures. Benefiting from SAM, multi-level semantics can be selectively propagated to predict precise salient objects. Furthermore, although integrating multi-level convolutional features can alleviate the blur boundary problem to a certain extent, it is still unsatisfactory for the restoration of a real object boundary. Therefore, we design a simple but effective boundary enhancement module (BEM) to guide the network focus on boundaries and other error-prone parts. Based on BEM, an attention weighted loss is proposed to boost the network to generate sharper object boundaries. Experimental results on five benchmark datasets demonstrate that the proposed method can achieve state-of-the-art performance on salient object detection. Moreover, we extend the experiments on the remote sensing datasets, and the results further prove the universality and scalability of ALNet.

Pathological characteristics of Chinese soft-shelled turtle (Pelodiscus sinensis) with white abdominal disease

White abdominal disease is one of the major diseases in Chinese soft-shelled turtle (Pelodiscus sinensis, SST) aquaculture. To clarify the pathological characteristics of white abdominal disease, 24 SSTs with typical white abdominal symptoms were collected from a farm in Hubei Province, China. First, the pathogens of the SSTs were detected, including viruses and bacteria. No virus was detected. A variety of bacteria have been isolated. The pathological characteristics of the diseased SSTs were then studied using hematological analysis, histopathological observation, intestinal microbial analysis, and immunity-related gene expression. Compared with the healthy SST, the number of red blood cells reduced from 1.2 ± 0.5 × 1013 /L to 1.5 ± 0.2 × 1012 /L in the diseased SSTs. Meanwhile, the number of white blood cells increased by nearly four-fold (from 1.8 ± 0.5 × 1010 /L to 7.1 ± 3.5 × 1010 /L). In the diseased SSTs, the liver displayed several abnormalities, such as hepatocyte vacuolation and changes in nonparenchymal tissue. Some glomeruli of the kidney showed necrosis and disintegration. Cavities and loose muscles were observed in the gastric muscle layer. The blurred boundary between the red pulp and white pulp of the spleen was accompanied by massive invasion of inflammatory cells. Significant differences were detected in the relative abundances of Proteobacteria (from 29% to 7%) and Fusobacteria (from 12% to 28%) between the diseased and healthy SSTs. Compared with those in the healthy SSTs, the gene expression levels of toll-like receptor 2(TLR), TLR3, and myeloid differentiation primary response 88 (MyD88) were increased, by more than 4-fold, 7-fold, and 5-fold, respectively (P < 0.01) in the diseased SST liver. The TLR2, TLR4, and MyD88 expression levels increased significantly in the diseased SST kidney. These results indicated that the diseased SSTs experienced anemia, visceral tissue lesions, reduction of intestinal microbial species, and innate immune activation.

Sustaining the “Pink Ghetto”? The identity negotiations of Chinese women journalists in the field of digital journalism

ABSTRACT Feminist studies of the media have shown different degrees of gender inequality in journalism organizations. Drawing on the notions of field and gendered habitus, the authors of this study conducted in-depth interviews with 30 Chinese women journalists to explore their experiences of digital journalism production, their survival logic in the field, the associated gender power relationships, and the reconstruction of ‘Pink Ghetto’ in the digital news field. The research shows that women journalists with more opportunities for work and career development while generating various gender-related challenges related to blurred boundaries between work and family life, deviations from career paths, and the risks of using digital platforms. The study explains how women journalists’ identities negotiate between the gendered habitus and the professional identity. Those journalists construct multiple identities by adopting four strategies, namely ‘strong gender’, ‘weak gender’, ‘de-gendering’, and ‘trans-gender’, which proposed a framework of identity negotiation. The study thus offers a broad perspective in feminist media research in developing countries.