Mechanism Of Discrimination Research Articles

Designing for justice in freelancing: Testing platform interventions to minimise discrimination in online labour markets

Online labour markets (OLMs) are a vital source of income for globally diverse and dispersed freelancers. Despite their promise of neutrality, OLMs are known to perpetuate hiring discrimination, vested in how OLMs are designed and what kinds of interactions they enable between freelancers and hirers. In this study, we go beyond understanding mechanisms of hiring discrimination in OLMs, to identifying platform design features that can minimise hiring discrimination. To do so, we draw on a methodology guided by the design justice ethos. Drawing on a survey on UK-based freelancers and interviews with a purposefully drawn sub-sample, we collaboratively identify five platform design interventions to minimise hiring discrimination in OLMs: community composition, identity-signalling flairs, text only reviews, union membership, and an antidiscrimination prompt. The core of our study is an innovative experiment conducted on a purpose-built, mock OLM, Mock-Freelancer.com. On this mock OLM, we experimentally test mechanisms of discrimination, including how these mechanisms fare under the five altered platform design interventions through a discrete-choice experiment. We find that both community and flairs were important in encouraging the hiring of women and non-White freelancers. We also establish that anonymity universally disadvantages freelancers. We conclude with recommendations to design OLMs that minimise labour market discrimination.

External defect detection technology and application status of transmission and transformation equipment based on video images

Power transmission and distribution equipment plays a critical role in supplying electricity to consumers. However, these assets are susceptible to external defects, such as corrosion, mechanical damage, and wear, which can lead to failures and disruptions in the electrical grid. Traditional inspection methods for detecting these defects often rely on manual inspections, which are time-consuming, costly, and subjective. To overcome these limitations, this paper explores the current state of video image-based external defect detection techniques for power transmission and distribution equipment. This makes up for the deficiencies of conventional approaches to inspecting and maintaining power transmission and transformation equipment by decreasing the waste of human resources and increasing the frequency and efficiency of intelligent operation and maintenance of power systems. This work investigates a completely convolutional block detection-based defect identification method to address the issue of defect recognition. The fully convolutional neural network is enhanced with the concept of block detection thanks to this approach. The local discrimination mechanism may be realized, and the drawbacks of the conventional block detection receptive field are avoided in the process. This approach offers improved generalization and fault identification over the original ResNet image classification system.

RED-Net: Residual and Enhanced Discriminative Network for Image Steganalysis in the Internet of Medical Things and Telemedicine.

Internet of Medical Things (IoMT) and telemedicine technologies utilize computers, communications, and medical devices to facilitate off-site exchanges between specialists and patients, specialists, and medical staff. If the information communicated in IoMT is illegally steganography, tampered or leaked during transmission and storage, it will directly impact patient privacy or the consultation results with possible serious medical incidents. Steganalysis is of great significance for the identification of medical images transmitted illegally in IoMT and telemedicine. In this article, we propose a Residual and Enhanced Discriminative Network (RED-Net) for image steganalysis in the internet of medical things and telemedicine. RED-Net consists of a steganographic information enhancement module, a deep residual network, and steganographic information discriminative mechanism. Specifically, a steganographic information enhancement module is adopted by the RED-Net to boost the illegal steganographic signal in texturally complex high-dimensional medical image features. A deep residual network is utilized for steganographic feature extraction and compression. A steganographic information discriminative mechanism is employed by the deep residual network to enable it to recalibrate the steganographic features and drop high-frequency features that are mistaken for steganographic information. Experiments conducted on public and private datasets with data hiding payloads ranging from 0.1bpp/bpnzac-0.5bpp/bpnzac in the spatial and JPEG domain led to RED-Net's steganalysis error PE in the range of 0.0732-0.0010 and 0.231-0.026, respectively. In general, qualitative and quantitative results on public and private datasets demonstrate that the RED-Net outperforms 8 state-of-art steganography detectors.

Biosynthesis and function of 7-deazaguanine derivatives in bacteria and phages.

SUMMARYDeazaguanine modifications play multifaceted roles in the molecular biology of DNA and tRNA, shaping diverse yet essential biological processes, including the nuanced fine-tuning of translation efficiency and the intricate modulation of codon-anticodon interactions. Beyond their roles in translation, deazaguanine modifications contribute to cellular stress resistance, self-nonself discrimination mechanisms, and host evasion defenses, directly modulating the adaptability of living organisms. Deazaguanine moieties extend beyond nucleic acid modifications, manifesting in the structural diversity of biologically active natural products. Their roles in fundamental cellular processes and their presence in biologically active natural products underscore their versatility and pivotal contributions to the intricate web of molecular interactions within living organisms. Here, we discuss the current understanding of the biosynthesis and multifaceted functions of deazaguanines, shedding light on their diverse and dynamic roles in the molecular landscape of life.

Racial Discrimination and Administrative Burden in Access to Public Services

Equal access to public services is a foundational element of democratic societies. Yet, stark inequalities in access to public services persist, partially due to bureaucratic discrimination, or differential treatment by bureaucrats. This study investigates the causal mechanism of bureaucratic discrimination, arguing that racial discrimination can serve as a means of cream skimming, when there are economic incentives to prioritize easier-to-serve clientele. We predict that in the absence of information regarding prospective clients’ performance, group-level performance information will be imposed on racially minoritized individuals. We implemented a nationwide email correspondence audit experiment including all charter school principals in the U.S. (n = 5850). The findings show that Black email aliases faced significantly higher administrative burdens in trying to get access to charter schools than White email aliases when no performance signal was provided. However, when a direct signal of clients’ performance was introduced, the racial disparities diminished. Overall, these results provide evidence on the causal mechanism of bureaucratic discrimination as a means of cream-skimming.

Specificity of carbon nanotube accumulation and distribution in cancer cells revealed by K-means clustering and principal component analysis of Raman spectra.

Single-walled carbon nanotubes (SWCNTs) show great potential for their application as cancer therapeutic nanodrugs, but the efficiency and mechanism of their accumulation in the cell, the modulation of cell activity, and the strong dependence of the results on the type of capping molecule still hinder the transfer of SWCNTs to the clinic. In the present study, we determined the mechanism and sequence of accumulation, distribution and type discrimination of SWCNTs in glioma cells by applying K-means clustering and principal component analysis (PCA) of Raman spectra of cells exposed to SWCNTs capped with either DNA or oligonucleotides (ON). Based on the specific biochemical information uncovered by PCA and further applied to K-means, we show that the accumulation of SWCNT-DNA occurs in two phases. The first phase involves the transport of SWCNT-DNA through vesicles and its redistribution in the cytoplasm, which is reflected in two SWCNT-related clusters. The second phase begins after 18 hours of interaction between cells and SWCNT-DNA. PCA shows the appearance of two SWCNT-associated PC loadings, reflected by the addition of a new cluster of SWCNTs with a narrowed and shifted G-peak in the spectra. It is caused by the loss of DNA capping and clumping of SWCNTs and triggered by the acidic conditions in autolysosomes resulting from the fusion of transport vesicles with lysosomes. SWCNTs penetrate all cellular compartments after 42-66 hours and lead to cell death. The clumped SWCNTs are released to the outside. In contrast, SWCNT-ON is hardly accumulated in glioma cells and after 72 hours of exposure to SWCNT-ON, the accumulation of SWCNTs corresponds to the first stage without reaching the second. PCA made it possible to separate the characteristics of cellular components against the high-intensity Raman signal from nanotubes and, thus, to propose the mechanism of accumulation and metabolism of nanomaterials in living cells without the use of additional research approaches. Our results elucidate the time dependence of the accumulation of SWCNTs on the capping molecule. We expect that our results can make an important contribution to the use of these nanomaterials in the clinic.

“Resilience of Northeast Migrants in Chennai: Adversity to Stability”

The migration of individuals from Northeast India to metropolitan cities like Chennai has increased significantly over the past two decades. This study aims to explore different factors that promotes resilience among Northeast migrants in Chennai, focusing on their socio-cultural adaptation, economic integration, and coping mechanisms in the face of structural and social challenges. Through a mixed-methods approach combining surveys and in-depth semi structure interviews and secondary data of existing literature review on migrant resilience, focusing on social support, discrimination, and coping mechanisms. The study investigates key factors that contribute to the resilience of migrants in navigating a highly diverse and challenging new sociocultural and religious environment of Chennai. Findings indicate that while migrants face significant discrimination, economic hardship, and cultural isolation, they develop strong support networks, local populace supports, and a collective sense of identity to cope with these challenges. The study concludes that resilience among Northeast migrants in Chennai is a dynamic, multi-faceted process driven by individuals, Non-governmental Organizations, local people support and community cohesion.

Unwanted Neighbours, Unwanted Memory. Slovak–Roma Relations in Marek Vadas’s Six Strangers

In contemporary Slovakia, the Roma population are often seen as unwanted neighbours – a marginalised community, which experiences discrimination in various spheres of life. Anti-Romani sentiment, which constitutes the basis of negative attitudes to- ward the Roma minority, is hardly a new phenomenon; its manifestations, including specific acts of violence, can be found in the past. One of the examples of this kind of violence – the bloody pogrom in Pobedim carried out against the Romani populace by their Slovak neighbours in 1928 – offer a starting point for Marek Vadas’s Six Strangers (Šesť cudzincov, 2021). The historic site of the massacre, which is not commemorated in any form, has be- come a non-site of memory, while the tragic events have been pushed out of Slovak historical consciousness. Vadas’s prose is an attempt to bring them back to the collective conscious- ness and raise a number of important questions concerning the operation of cultural codes that permit and justify violence, the position and responsibility of the bystanders, as well as silence as a form of complicity in acts of aggression. In addition, it introduces a contemporary perspective, pointing to the persistence of mechanisms of discrimination, stigmatisation and exclusion of the Others, understood in many different ways, from the community.

Fault detection of high-speed train axle bearings based on a hybridized physical and data-driven temperature model

As a key component connecting axle and frame, axle bearings of the high-speed train are essential for the safe operation of modern high-speed trains. The current on-board fault detection system, which compares the axle box temperature measured from the sensor to a predefined threshold value, faces problems such as poor adaptability to varied service conditions, undesired high rate of false alarms and missing alarms, and lack of potential to be optimized. To solve these problems, in the present study, a novel hybrid model is proposed which predicts the temperature of a healthy bearing under the given service condition, and based on that detects the bearing fault. The hybrid model includes a physical model reflecting the thermal behavior of the axle box and two data-driven models adopting the same inputs but representing the relationship by means of back-propagation (BP) neural network and long-short term memory neural network (LSTM). After calibrating these models by real-life operation data collected from 2.25 million kilometers and using all results as inputs, a fault discriminant mechanism is established. The performance of this new mechanism is superior than the current on-board system and all stand-alone models in terms of indicators such as precision, recall and F1 score. This way, the study shows that similar hybrid models can be a viable solution for the future on-board fault detection system.

RESIDENTIAL SEGREGATION AND DEPRESSIVE SYMPTOMS IN OLDER CHINESE IMMIGRANTS: MEDIATING ROLE OF SOCIAL PROCESSES

Abstract Older Chinese immigrants are particularly at risk for depression due to acculturative stress and language barriers. Residential segregation with respect to language use may play an important role in mental health of historically marginalized immigrant populations. Previous research provided mixed evidence about the segregation effect among older Latino and Asian immigrants. Guided by a model of social processes, we examined the direct and indirect effects of residential segregation on depressive symptoms via multiple mechanisms of acculturation, discrimination, social network, social support, social strain, and social engagement. Four waves of depressive symptoms were assessed in the Population Study of Chinese Elderly (2011-2019, N=1,970), and linked to the 2010-2014 American Community Survey estimates of neighborhood context. Residential segregation was measured by the Index of Concentrations at the Extremes that simultaneously assesses Chinese and English language use within a given census tract. Latent growth curve models with adjusted cluster robust standard errors were estimated after controlling for individual-level factors. Residents of segregated Chinese-speaking neighborhoods had fewer baseline depressive symptoms but a slower decline rate than those living in segregated toward English-only speaking neighborhoods. Racial discrimination, social strain, and social engagement partially mediated the association between segregation and initial level; social strain and social engagement also partially mediated the association with decline in depressive symptoms. This study demonstrates the importance of residential segregation and social processes in shaping mental well-being among older Chinese immigrants and suggests potential mechanisms to alleviate mental health risks.

Discrimination against migrants and its determinants: Evidence from a Multi-Purpose Field Experiment in the Housing Rental Market

The increasing number of Venezuelan migrants in Colombia, which now exceeds 2.9 million, has raised concerns about their potential discrimination and socioeconomic integration. We propose a novel approach to studying discrimination in the housing rental market by conducting a multi-purpose field experiment with 574 real estate agents to, not only measure the extent of discrimination but also to explore its determinants. After gathering comprehensive data about the Real Estate Agents (REAs), they evaluated rental applications from Colombian, Venezuelan, and other nationality families. Our findings suggest the presence of discrimination in the housing rental market, as REAs were less likely to choose a Venezuelan family over a Colombian or other nationality family. We identified that this discrimination was influenced by factors such as age, gender, cognitive skills, and local market knowledge. We found that the discriminatory social norm is well recognized among REAs. This study has significant implications for policymakers, advocates, and practitioners, not only in Colombia but globally, as it sheds light on the mechanisms of discrimination against migrant populations and emphasizes the need to promote their integration and well-being.

HSNF: Hybrid sampling with two-step noise filtering for imbalanced data classification

Imbalanced data classification has received much attention in machine learning, and many oversampling methods exist to solve this problem. However, these methods may suffer from insufficient noise filtering, overlap between synthetic and original samples, etc., resulting in degradation of classification performance. To this end, we propose a hybrid sampling with two-step noise filtering (HSNF) method in this paper, which consists of three modules. In the first module, HSNF denoises twice according to different noise discrimination mechanisms. Note that denoising mechanism is essentially based on the Euclidean distance between samples. Then in the second module, the minority class samples are divided into two categories, boundary samples and safe samples, respectively, and a portion of the boundary majority class samples are removed. In the third module, different oversampling methods are used to synthesize instances for boundary minority class samples and safe minority class samples. Experimental results on synthetic data and benchmark datasets demonstrate the effectiveness of HSNF in comparison with several popular methods. The code of HSNF will be released.

Non-native Accents among School Beginners and Teacher Expectations for Future Student Achievements

Based on sociological, economic, and social-psychological theories of discrimination and bias, this study addresses non-native accents among ethnic minority students as they begin school and explores effects of such accents on their teachers’ achievement expectations. Using a unique data set of first graders in Germany, the analysis reveals that a non-native accent is relevant to teachers’ expectations net of student skills, abilities, and other background variables. Associations are stronger in the language domain than in mathematics, indicating that teachers perceive accent-free speech as a language-learning requirement. However, residual influences of non-native accents on teacher expectations also exist in the math domain and persist even after prolonged periods of teacher-student interaction. Mechanisms of statistical discrimination and stereotype-based discrimination can partially explain these effects. However, the overall pattern of results suggests a stigmatization of non-native accents, potentially resulting from the activation of negative associations related to foreignness and disfluency.

Banking for the Other Half: The Factors That Explain Banking Desert Formation

Banks are one of the key drivers of economic development across communities. Banking deserts—defined by inadequate banking access—limit access to capital, inhibit wealth accumulation, and increase exposure to predatory lending. Banking desert formation could be profit-driven, with lower-income and less densely populated regions more likely to become banking deserts. Discrimination could also play a role here: banks may have less presence in areas with higher minority populations. The authors use a panel, census tract-level data set for the entire state of North Carolina to investigate how these forces impact banking access and banking desert formation. Panel methodologies are incorporated to investigate the extent to which profit and discrimination mechanisms each drive banking access and banking desert formation. Profit and discrimination mechanisms are shown to play roles, highlighting the need for policies that mitigate bank branch losses in underserved neighborhoods.

Love thy (Ukrainian) neighbour: Willingness to help refugees depends on their origin and is mediated by perceptions of similarity and threat.

Prejudice and discrimination against minorities can be a powerful tool for populistic and reactionary political movements, and it is therefore crucial to study its determinants. The aim of this research is to develop the understanding of a possible mechanism of such discrimination: cultural distance. In a pre-registered survey experiment with a between-subjects design, we draw on the large increase in intra-European refugee migration from Ukraine, to test whether refugees from another ongoing conflict in (culturally distant) Yemen are treated differently than (culturally similar) Ukrainian refugees by British participants (N = 1545). We measured stated willingness to help and to hire refugees. Moreover, the participants were offered the chance to donate their own earnings from survey participation to real charity drives aimed at the respective refugee groups. Thus, we are able to examine both stated and actual helping behaviours that captured both autonomy- and dependency-oriented forms of helping. As expected, participants were more willing to help, hire and donate money to Ukrainian refugees, and these effects were mediated by higher perceived similarity and lower perceived threat from Ukrainians compared with Yemenis.

Fusion detection network with discriminative enhancement for weakly-supervised temporal action localization

Weakly-supervised temporal action localization aims to identify and localize action instances in untrimmed videos using only video-level action labels. Due to the lack of frame-level annotation information, correctly distinguishing foreground and background snippets in a video is crucial for temporal action localization. However, alongside foreground and background snippets, a large number of semantically similar snippets exist within the video. Such snippets share the same semantic information with foreground or background, leading to less fine-grained boundary localization of action instances. Inspired by the success of multimodal learning, we have extracted high-quality semantic features from multimodal inputs and constructed contrast loss to enhance the ability of the model to distinguish semantically similar snippets. In this paper, we propose a fusion detection network with discriminative enhancement(De-FDN). Specifically, we design a fusion detection model (FDM) that fully leverages the complementarity and correlation among multimodal features to extract high-quality semantic features from videos. We then construct multimodal class activation sequences to accomplish accurate identification and localization of action instances. Additionally, we design a discriminative enhancement mechanism (DEM), which increases the gap between semantically similar segments by calculating the semantic contrast loss. Extensive experiments on the THUMOS14, ActivityNet1.2, and ActivityNet1.3 datasets demonstrate the effectiveness of our method.

The possibility of radical resources and participation in architectural education: autobiographical spatial narratives

PurposeThis paper aims to contribute to the pedagogical field of architectural education by conceptualizing autobiographical spatial narratives as possible radical resources and avenues for participation. It seeks to advance a critical approach to the dominant canon of course contents and hidden local dynamics of exclusion and discrimination in architectural education.Design/methodology/approachThe methodology is based on conceptual and critical analyses of feminist, postcolonial and radical architectural pedagogies, relating those with broader feminist pedagogies that question exclusion and discrimination mechanisms from the perspective of the radicality of emotions. As a second step, three experiments intentionally designed in academic courses to open space for autobiographical spatial narratives are analysed to extend the theoretical discussion into the specific local dynamics of exclusion and discrimination that have largely been ignored to date in Turkey.FindingsDifferent pedagogical approaches and self-experiments have revealed that autobiographical spatial narratives are a type of resource that accommodates students' diverse spatial experiences including forcible displacement. Sharing that multiplicity creates opportunities for participation in the classroom and studio where different individualities, backgrounds and identities are made visible. These potential resources and participation are open to emotions and affects, are collective and transformative and, therefore, are radical.Research limitations/implicationsAlthough research on architectural pedagogies is still limited, the current literature is constantly being empowered by new studies from various geographies and localities. The present study may facilitate future comparative readings and further research on radical architectural pedagogies, particularly within the Global South, where complex local dynamics might share commonalities dominated by the Western canon. It may also open new discussions on discrimination and the exclusion of silenced individuals in architectural education in Turkey and elsewhere. In the scope of this paper, however, the practical experiences and observations based on two years in architectural education may be too limited for a comprehensive analysis of the applications of autobiographical spatial narratives.Originality/valueThis paper offers novel strategies for creating inclusive, intersectional and decolonized perspectives for knowledge production and more equal spaces in architectural education.

Multi-Attention Infused Integrated Facial Attribute Editing Model: Enhancing the Robustness of Facial Attribute Manipulation

Facial attribute editing refers to the task of modifying facial images by altering specific target facial attributes. Existing approaches typically rely on the combination of generative adversarial networks and encoder–decoder architectures to tackle this problem. However, current methods may exhibit limited accuracy when dealing with certain attributes. The primary objective of this research is to enhance facial image modification based on user-specified target facial attributes, such as hair color, beard removal, or gender transformation. During the editing process, it is crucial to selectively modify only the regions relevant to the target attributes while preserving the details of other unrelated facial attributes. This ensures that the editing results appear more natural and realistic. This study introduces a novel approach called MAGAN (Combining GRU Structure and Additive Attention with AGU—Adaptive Gated Units). Moreover, a discriminative attention mechanism is introduced to automatically identify key regions in the input images that are relevant to facial attributes. This mechanism concentrates attention on these regions, enhancing the model’s ability to accurately capture and analyze subtle facial attribute features. The method incorporates external attention within the convolutional layers of the encoder–decoder architecture, facilitating the modeling of linear complexity across image regions and implicitly considering correlations among all data samples. By employing discriminative attention in the discriminator, the model achieves more precise attribute editing. To evaluate the effectiveness of MAGAN, experiments were conducted on the CelebA dataset. The average precision of facial attribute generation in images edited by our model stands at 91.83%. PSNR and SSIM for reconstructed images are 32.52 and 0.957, respectively. In comparison with existing methodologies (AttGAN, STGAN, MUGAN), noteworthy enhancements have been achieved in the domain of facial attribute manipulation.

An improved probabilistic spiking neural network with enhanced discriminative ability

The non-differentiability of the spike activity has been a hindrance to the development of high-performance spiking neural networks (SNNs). Current learning algorithms mainly focus on achieving attractive SNNs based on surrogate gradient or conversion, yet their performance is still limited. The probability-based SNNs use the probabilistic mechanism to smooth out spike activity, showing a promising way for training SNNs. This work optimizes the probabilistic mechanism and proposes the probabilistic firing mechanism (PFM) for spiking neurons. PFM enables differentiable spike activity and can be adapted to a variety of spiking neurons. In addition, to eliminate the negative influence of probabilistic uncertainty, the attention discrimination mechanism (ADM) is proposed, which enables the neurons to respond efficiently by adaptively distinguishing the salient elements of the input current. By fusing PFM, ADM, and Leaky Integrate-and-Fire (LIF) neurons, we constructed the Probabilistic Attention Leaky Integrate-and-Fire (PALIF) neuron and Probabilistic Attention Spiking Neural Network (PASNN). Ablation studies confirm the effectiveness of PFM and ADM, and indicate that PASNN is suitable for low-latency scenarios. Experiments on both static image and neuromorphic datasets, including CIFAR10, CIFAR100, N-MNIST, and CIFAR10-DVS, demonstrate that PASNN achieves competitive performance in terms of accuracy and inference speed.

Türk Evliliğinde Dilsel Örüntülerle Kadına Yönelik Ayrımcılığın İncelenmesi

This research embarks on a complicated journey to uncover the gender within the institution of Turkish marriage, focusing on the linguistic manifestations of discrimination against women. Through critical analysis of conversations between married individuals, the study seeks to illuminate how marital dialogs not only share everyday experiences, but also uphold, challenge, or change deeply rooted social norms and prejudices. Participants were tasked with delineating perceived gender roles within marital boundaries. Their responses, juxtaposed with the recorded conversations of 10 married couples — all from the researcher’s immediate circle — offer a multi-layered insight into the implicit biases and potential microaggressions inherent in everyday interactions. This methodological choice was anchored in the belief that genuine dialogs from familiar contexts would reveal both overt and covert forms of gender discrimination.
 
 By analyzing these recordings, the study aims to identify overarching characteristics and differences in the conversational styles of men and women. Preliminary findings suggest that linguistic markers may unintentionally perpetuate traditional gender roles and biases, underscoring subtle but persistent mechanisms of discrimination against women in Turkish society. Through this research, we aim to shed light on the deeper linguistic structures at play and their implications for broader social change.