Identity Labels Research Articles

Novel adaptive loss weighted transfer network for partial domain fault diagnosis

Mechanical fault transfer diagnosis has been confirmed as a feasible approach for tackling intelligent diagnosis with incomplete fault information and scarce labeled data on the basis of big data through the transfer of diagnostic knowledge from one or more conditions to any other condition. However, existing research has developed a hypothesis, i.e., the target domain shares an identical label space with the source domain, making it unfeasible to address the practical issue that the target domain label space is a subset of the source domain label space, resulting in low transfer diagnosis accuracy. To address this issue, a novel unsupervised intelligent diagnosis approach named double classifiers-dependent transfer diagnosis network is developed. In this approach, the label distribution weights are generated through the probability output of the classifier of source domain label space to target domain samples, by which small weights are assigned to irrelevant source samples to avoid negative transfer in the global-local maximum mean discrepancies (GL-MMD). In addition, classifiers of the source domain label space and the shared label space are built separately to improve the reliability of label distribution weights and GL-MMD. By training the network in the shared label space, diagnostic knowledge in partial domain issues is effectively transferred. Two cases are implemented to verify the effectiveness of the developed approach. Compared with other transfer diagnosis approaches, the developed approach achieved better diagnostic performance.

Learning Semantics-Consistent Stripes With Self-Refinement for Person Re-Identification.

Aligning human parts automatically is one of the most challenging problems for person re-identification (re-ID). Recently, the stripe-based methods, which equally partition the person images into the fixed stripes for aligned representation learning, have achieved great success. However, the stripes with fixed height and position cannot well handle the misalignment problems caused by inaccurate detection and occlusion and may introduce much background noise. In this article, we aim at learning adaptive stripes with foreground refinement to achieve pixel-level part alignment by only using person identity labels for person re-ID and make two contributions. 1) A semantics-consistent stripe learning method (SCS). Given an image, SCS partitions it into adaptive horizontal stripes and each stripe is corresponding to a specific semantic part. Specifically, SCS iterates between two processes: i) clustering the rows to human parts or background to generate the pseudo-part labels of rows and ii) learning a row classifier to partition a person image, which is supervised by the latest pseudo-labels. This iterative scheme guarantees the accuracy of the learned image partition. 2) A self-refinement method (SCS+) to remove the background noise in stripes. We employ the above row classifier to generate the probabilities of pixels belonging to human parts (foreground) or background, which is called the class activation map (CAM). Only the most confident areas from the CAM are assigned with foreground/background labels to guide the human part refinement. Finally, by intersecting the semantics-consistent stripes with the foreground areas, SCS+ locates the human parts at pixel-level, obtaining a more robust part-aligned representation. Extensive experiments validate that SCS+ sets the new state-of-the-art performance on three widely used datasets including Market-1501, DukeMTMC-reID, and CUHK03-NP.

Multi target tracking algorithm based on anchor free detection

In order to alleviate the problems of frequent identity label switching caused by mutual occlusion between targets and missed detection caused by small target sizes in multi-target tracking, this paper proposes a multi-target tracking algorithm based on anchor-free detection. This algorithm first uses YOLOX for detection and embeds the ReID algorithm to enhance pedestrian recognition. Then, it uses the DeepSORT algorithm for tracking and correcting abnormal trajectory algorithms for occluded targets. Finally, experiments are conducted on both the MOT16 dataset and the self-made dataset. This algorithm has higher tracking accuracy than other algorithms, and its tracking performance for small targets has also been effectively improved.

Adversarially Learning Occlusions by Backpropagation for Face Recognition.

With the accomplishment of deep neural networks, face recognition methods have achieved great success in research and are now being applied at a human level. However, existing face recognition models fail to achieve state-of-the-art performance in recognizing occluded face images, which are common scenarios captured in the real world. One of the potential reasons for this is the lack of large-scale training datasets, requiring labour-intensive and costly labelling of the occlusions. To resolve these issues, we propose an Adversarially Learning Occlusions by Backpropagation (ALOB) model, a simple yet powerful double-network framework used to mitigate manual labelling by contrastively learning the corrupted features against personal identity labels, thereby maximizing the loss. To investigate the performance of the proposed method, we compared our model to the existing state-of-the-art methods, which function under the supervision of occlusion learning, in various experiments. Extensive experimentation on LFW, AR, MFR2, and other synthetic masked or occluded datasets confirmed the effectiveness of the proposed model in occluded face recognition by sustaining better results in terms of masked face recognition and general face recognition. For the AR datasets, the ALOB model outperformed other advanced methods by obtaining a 100% recognition rate for images with sunglasses (protocols 1 and 2). We also achieved the highest accuracies of 94.87%, 92.05%, 78.93%, and 71.57% TAR@FAR = 1 × 10-3 in LFW-OCC-2.0 and LFW-OCC-3.0, respectively. Furthermore, the proposed method generalizes well in terms of FR and MFR, yielding superior results in three datasets, LFW, LFW-Masked, and MFR2, and producing accuracies of 98.77%, 97.62%, and 93.76%, respectively.

“Afghan” in Afghanistan: Idols in the Land of Idols

A perennial anomaly in the academic, political, and popular discourse about “Afghan” and “Afghanistan” is the chronic absence of an informed and systematic engagement with the epistemology and semantic construction of the Persian identity label “Afghan” and its derivative use in the cultural, political, social, and spatial configuration of “Afghanistan.” This article offers a brief overview of, and a corrective to, this cultural and linguistic disposition. The essay examines the widespread use, the cultural and historical roots, and linguistic seeds of the ethnonym “Afghan” and its derivative toponymal “Afghanistan.” The essay excavates the Buddhist stance and the epistemological cradle of the Persian morpheme from which the term “Afghan” is derived. This exercise in the ethnology of Afghanistan locates the country and its people in historical “deep time” and in the context of several cultural layers and processes rather than a cultural construct in isolation. It aims to serve as a springboard for a corrective etymological awareness for the production of academic and political texts and discourse dealing with Afghanistan and its people.

Demographic predictors of experiences of homelessness among lesbian, gay, bisexual, trans, gender-diverse and queer-identifying (LGBTIQ) young people in Australia

ABSTRACT Homelessness among young lesbian, gay, bisexual, trans, gender-diverse and queer-identifying (LGBTQ+) persons is highly prevalent and constitutes a structural risk to health and future life chances. However, the distribution of homelessness burden is among different LGBTQ+ subgroups is poorly understood. An Australia-wide cross-sectional online survey was conducted involving 6,481 LGBTQ+ participants aged 14–21 years during 2019. Single-predictor logistic regression analyses identified factors associated with both lifetime and recent experiences of homelessness. Analyses also explored associations between recent (<12 months) experiences of homelessness, experiences of harassment, alcohol consumption, and psychological distress. Higher odds of experiencing homelessness were observed for trans and gender-diverse young people, individuals who identified with sexual identity labels other than lesbian, gay or bisexual, racially-minoritized persons, disabled persons and individuals from a religious family or household, compared to their respective counterparts. Experiencing homelessness was associated with higher levels of alcohol consumption and higher prevalence of experiencing verbal, physical and sexual harassment, but only modestly associated with higher levels of psychological distress. Homelessness risk and burden is unevenly distributed among LGBTQ+ youth and is linked to outcomes which may potentiate future homelessness. Interventions addressing homelessness among this group must be optimized for those subgroups most vulnerable to experiencing homelessness.

Improving the Transferability of Deep Learning Models for Crop Yield Prediction: A Partial Domain Adaptation Approach

Over the past few years, there has been extensive exploration of machine learning (ML), especially deep learning (DL), for crop yield prediction, resulting in impressive levels of accuracy. However, such models are highly dependent on training samples with ground truth labels (i.e., crop yield records), which are not available in some regions. Additionally, due to the existence of domain shifts between different spatial regions, DL models trained within one region (i.e., source domain) tend to have poor performance when directly applied to other regions (i.e., target domain). Unsupervised domain adaptation (UDA) has become a promising strategy to improve the transferability of DL models by aligning the feature distributions in the source domain and the target domain. Despite the success, existing UDA models generally assume an identical label space across different domains. This assumption can be invalid in crop yield prediction scenarios, as crop yields can vary significantly in heterogeneous regions. Due to the mismatch between label spaces, negative transfer may occur if the entire source and target domains are forced to align. To address this issue, we proposed a novel partial domain adversarial neural network (PDANN), which relaxes the assumption of fully, equally shared label spaces across domains by downweighing the outlier source samples. Specifically, during model training, the PDANN weighs each labeled source sample based on the likelihood of its yield value given the expected target yield distribution. Instead of aligning the target domain to the entire source domain, the PDANN model downweighs the outlier source samples and performs partial weighted alignment of the target domain to the source domain. As a result, the negative transfer caused by source samples in the outlier label space would be alleviated. In this study, we assessed the model’s performance on predicting yields for two main commodities in the U.S., including corn and soybean, using the U.S. corn belt as the study region. The counties under study were divided into two distinct ecological zones and alternatively used as the source and target domains. Feature variables, including time-series vegetation indices (VIs) and sequential meteorological variables, were collected and aggregated at the county level. Next, the PDANN model was trained with the extracted features and corresponding crop yield records from the U.S. Department of Agriculture (USDA). Finally, the trained model was evaluated for three testing years from 2019 to 2021. The experimental results showed that the developed PDANN model had achieved a mean coefficient of determination (R2) of 0.70 and 0.67, respectively, in predicting corn and soybean yields, outperforming three other ML and UDA models by a large margin from 6% to 46%. As the first study performing partial domain adaptation for crop yield prediction, this research demonstrates a novel solution for addressing negative transfer and improving DL models’ transferability on crop yield prediction.

Challenging the legacy of the past and present intimate colonialization – a study of Ugandan LGBT+ activism in times of shrinking communicative space

ABSTRACT Through a mixed-methods approach consisting of a directed content analysis of five established LGBT+ organizations’ use of Twitter and Facebook during a month in 2022, and semi-structured qualitative interviews with social media content producers, the study attempts to understand the role of self-controlled social media spaces in challenging the Uganda society’s logics of oppression. The results indicate that self-controlled spaces are not used for disrupting the basis for repression – the local logic of oppression – or its cocoon of collective post-colonial amnesia. Nor were spaces used for re-constructive engaging with transnational and development partners’ unwitting impact on global south actors’ agency and legitimacy. Instead, with a few exceptions, spaces displayed a conspicuous uniform human rights advocacy rhetoric, and Western identity labels summarized in the LGBT+ acronym. The interviews with social media content producers suggest that the LGBT+ community’s dependency on international support may sway actors into what we call performative visibility, in self-controlled spaces. The study concludes that future analysis of Global South based activist’s use of social media spaces’ affordances including its potential for supporting de-colonialization efforts, must approach use as relational to actors’ dependency on key resources such as funding and protection through affiliation.

Making sense of identity transitioning within the new venture creation process

PurposeThis study aims to explore how aspiring entrepreneurs navigate between their own individual self-concept and the organisational identity of the new venture during the process of new venture creation.Design/methodology/approachThe study draws on three cases of aspiring entrepreneurs within a UK-based university incubator in the process of “becoming” entrepreneurs. Semi-structured interviews and secondary data were collected and analysed using a flexible pattern matching approach.FindingsThe data illustrated parallel identity and sensemaking processes occurring as the aspiring entrepreneurs navigated towards new venture formation. For the organisational identity process, three key stages were found to occur: referent identity labelling, projection and identity reification. Concurrently the sensemaking process made up of creation, interpretation and enactment were seen to enable identity transitioning mechanisms: cue identification, liminal sensegiving and recognition of formal venture boundaries, which led to the organisational identity being formed.Research limitations/implicationsThis study is exploratory in nature thus future research is required to clarify the relationship between identity work practices and the process of creating a new venture (Oliver and Vough, 2020). The paper is limited to successful instances of new venture formation, and though this helped to extricate the identity transitioning stages and mechanisms that have thus far remained implicit within the process of new venture creation, it could be extended to examine entrepreneurs who fail to set up new ventures. This limitation opens avenues for further research on identity formation in failed ventures (Snihur and Clarysse, 2022) and on how entrepreneurs negotiate contested identities (Varlander et al., 2020). Furthermore, entrepreneurs take different pathways to new venture formation (Shepherd et al., 2021) and while this study follows the journey of aspiring entrepreneurs who differed in terms of sector, education and prior entrepreneurial experience (Shane, 2003), future researchers could undertake a more in-depth ethnographic study including the effects of incubator setting and how these can be best supported, as this was outside the original remit of this study. Given the importance of the university incubator (Bergman and McMullen, 2022), its role in the construction of new venture identity is an interesting area for future research.Practical implicationsThis study provides a practical contribution into entrepreneurship curricula and incubator training, emphasising the importance of understanding the relevance of the entrepreneur's self-concept in making sense of future venture identities. Through the findings of this study, the importance of cue identification and how aspiring entrepreneurs rely on these to carve out the identity of their budding venture is demonstrated. Incubator spaces may have a role to play in supporting aspiring entrepreneurs to reflect on and interpret feedback (liminal sensegiving) during the venture creation process. Furthermore, both educators and incubator managers need to be aware of the state of in-between-ness aspiring entrepreneurs will face as they carve out the identity of the budding venture. This study enables educators to advise aspiring entrepreneurs that there will come a point on the entrepreneurial journey when they need to emphasise boundary setting between self and organisation to enable organisational identity to be fostered and venture formation realised. This study advises incubator managers to consider whether support around business registrations and creation of business accounts should be provided earlier in the incubation programme to emphasise boundary setting between self and organisation. There is a fruitful avenue for future research to extend the work in this paper to fully understand how this might be taught and practiced in the classrooms.Originality/valueBy extricating the stages of organisational identity formation, often hidden within the new venture creation process, this study has framed new venture creation as a liminal experience and a visible site of identity work. This study presents a process model of the key identity transitioning stages and mechanisms in new ventures, by illustrating how aspiring entrepreneurs' sensemaking influences identity transitions during the process of venture creation.

Identifying suicide documentation in clinical notes through zero-shot learning.

In deep learning, a major difficulty in identifying suicidality and its risk factors in clinical notes is the lack of training samples given the small number of true positive instances among the number of patients screened. This paper describes a novel methodology that identifies suicidality in clinical notes by addressing this data sparsity issue through zero-shot learning. Our general aim was to develop a tool that leveraged zero-shot learning to effectively identify suicidality documentation in all types of clinical notes. US Veterans Affairs clinical notes served as data. The training data set label was determined using diagnostic codes of suicide attempt and self-harm. We used a base string associated with the target label of suicidality to provide auxiliary information by narrowing the positive training cases to those containing the base string. We trained a deep neural network by mapping the training documents' contents to a semantic space. For comparison, we trained another deep neural network using the identical training data set labels, and bag-of-words features. The zero-shot learning model outperformed the baseline model in terms of area under the curve, sensitivity, specificity, and positive predictive value at multiple probability thresholds. In applying a 0.90 probability threshold, the methodology identified notes documenting suicidality but not associated with a relevant ICD-10-CM code, with 94% accuracy. This method can effectively identify suicidality without manual annotation.

Sexual minorities responding to sexual orientation distress: Examining 33 methods and the effects of sexual identity labeling and theological viewpoint.

Sexual minorities responding to sexual orientation distress: Examining 33 methods and the effects of sexual identity labeling and theological viewpoint.

From continua to kaleidoscopes: How plurisexuality challenges traditional conceptualizations of sexual orientation

Traditional measures of sexual orientation (e.g., the Kinsey Scale, the Klein Sexual Orientation Grid, single-item sexual identity questions) focus on a person’s behaviour, identity, and attraction yet are described by plurisexual, asexual, and gender-diverse individuals as inadequate in capturing the complexity of how they experience and understand their sexuality. This study explores how plurisexual and asexual individuals make sense of their sexual orientation. Across several studies, participants (n = 2,306) completed demographic questionnaires related to their gender and sexual identity while also responding to the Kinsey Scale. Additionally, participants wrote open-ended responses explaining why they had selected a particular sexual identity or Kinsey score earlier in the survey. The authors used an abductive reflective thematic analysis to review these open-ended responses, focusing on whether responses aligned with a dominant model of sexual orientation (i.e., behaviour-identity-attraction). While many participants described their choice of sexual identity label and Kinsey score using at least one aspect of the behaviour-identity-attraction trichotomy, not all responses fit within this model. For example, participants emphasized their attraction to individual characteristics other than gender/sex, distinguished between romantic and sexual attraction, and discussed plurisexuality and experiences of sexual fluidity. Traditional measures of sexual orientation may not be adequate when working with sexually diverse samples. Researchers should consider how best to conceptualize sexual orientation and other related constructs, including emotional/romantic attraction, sexual fluidity, and the role of gender expression in processes of attraction and identity construction.

Gambaran Pengelolaan Limbah Medis Padat pada Klinik Bidan Praktik Mandiri di Kecamatan Lempuing Kabupaten Ogan Komering Ilir Tahun 2023

WHO said that in 2010, unsafe injections still caused as many as 33,800 new HIV infections, 1.7 million hepatitis B infections, and 315,000 hepatitis C infections. Independent Practice Midwife Clinics are one source of solid medical waste generation. Knowledge of solid medical waste management at independent practicing midwife clinics in Lempuing District, Ogan Komering Ilir Regency in 2023. Research This type of research is descriptive research. This research was conducted in March-May 2023. The population in this study were all 38 independent practicing midwife clinics. The sample is total sampling, namely all independent practicing midwife clinics. The data collection tool is a checklist sheet. Variable sorting, container, storage, and transportation. Data analysis using Univariate Analysis. Sorting 38 (100%) fulfilled the requirements, containers for labeling infectious waste 8 (22%) and pharmaceutical 13 (44%), container grouping for infectious 15 (40%) and pharmaceutical 30 (70%) did not meet the requirements. Storage contained 23 (59%) separate rooms, 8 (21%) covered packaging, and 20 (53%) identity labeled packaging did not meet the requirements. Transportation 11 (29%) did not carry out transportation. by concluding that 38 (100%) have done proper sorting, for containers that have not been labeled infectious waste 8 (22%) and pharmaceutical 13 (44%), there is no separate storage room 23 (59%), there is the packaging does not have a cover 8 (21%), the packaging does not have an identity label 20 (53%). Transportation 11 (29%) did not carry out the transportation be consistent in carrying out proper sorting, more complete facilities, especially in terms of container and storage, and know how to process and transport medical waste properly and correctly.

Signed attention based graph neural network for graphs with heterophily

Graph Neural Networks (GNNs) are an effective deep learning methodology used to encode graph-structured data. Several popular GNNs have demonstrated excellent results in various downstream tasks, including node classification and visualization, especially when homophily occurs in graphs where connected nodes may share identical features or labels. Although recent models can extend GNNs beyond homophily, two fundamental weaknesses limit their expressive ability. First, most of the existing GNNs fail to adaptively aggregate information from different neighbors, especially negative information from different classes. Even worse, they fail to encode the positional information, leading to the inevitable conflation of information from different neighborhoods that should be specified with different importance. To overcome these limitations, we propose a novel graph neural network model based on the Signed Attention mechanism (SA), namely SAGNN. Specifically, the signed attention mechanism is presented to adaptively learn the complex relationship between exact neighboring nodes. Compared with the original non-negative attention mechanism, SA aggregates information from both positive and negative neighbors, especially on graphs with heterophily. Furthermore, we propose an Exactly Higher-Order Neighborhood Aggregation that implicitly encodes positional information to specify the importance of different neighborhoods. Extensive experiments conducted on eight benchmark datasets demonstrate that the proposed SAGNN model learns more robust and expressive representations, and achieves superior performance on node classification and visualization tasks.

Effects of transgender identity labels on perceptions of gender minorities

ABSTRACT Gender minorities disproportionately experience stigma and violence after coming out. This study was conducted to examine how individuals perceive characters identified with various gender identity labels (transgender man, transgender woman, genderfluid, non-binary, cisgender man, cisgender woman), to better understand the effects of these labels on people’s perceptions. Student participants (N = 498) were randomly assigned to one of the three conditions and read six vignettes briefly describing characters with the aforementioned identities. Conditions included explicit labels (e.g. ‘Mike is a transgender man’), implicit labels (e.g. ‘Mike was assigned female at birth, but now identifies as a man’), and no gender labels. Participants then rated each character on 19 different traits/attributes (e.g. ‘likable’, ‘perverted’, ‘masculine’). All characters were rated more positively when described with an explicit or implicit gender identity label in comparison to the no label control condition. The manipulation of gender labels had little influence on ratings of negative traits. These positive and encouraging results may be partially attributed to the sample which predominantly comprised young, liberal, women and may be indicative of a welcoming university environment for gender minorities. Future studies could further explore this topic by expanding the scope to the larger national population.

Theorising the collective in British estate literature

ABSTRACT This article attends to recent examples of British estate literature, a literary form which extends across disparate genres and media. In general, estate literature attempts to correct pernicious prejudices about communities centred on the council estate, stereotypes which align classist, racist, and sexist rhetoric to marginalise this population. In addition to locating recent examples of estate literature – Caleb Femi’s, Poor (2020), Anders Lustgarten’s The Seven Acts of Mercy (2016), and Guy Gunaratne’s In Our Mad and Furious City (2018) – amidst this socio-economic context, this paper also identifies the various formal features these works draw upon to generate a collective voice which at once rejects the othering of the council estate while also resisting the temptation to substitute one reductive group identity label for another. In these texts, often-surprising connections are formed on the grounds of the estate between people, objects, ideas, art, music – and fade as suddenly as they appear. The essay spans media studies, literary criticism, and sociology to argue that the networks conjured by estate literature are not only corrective but generative, inasmuch as they indicate that more affirmative configurations of these networks are possible.

A partial domain adaptation scheme based on weighted adversarial nets with improved CBAM for fault diagnosis of wind turbine gearbox

Most domain adaptation methods for fault diagnosis depend heavily on the precondition that the source and target domain have an identical label space, which is hard to be satisfied in industrial sites. Recently, many approaches have been developed to implement partial domain adaptation. However, most existing methods adopt classic convolutional neural network as the feature extractor, which limits the ability to learn discriminative representations from non-stationary vibration signals of wind turbine (WT) gearboxes. Moreover, the design of multiple subdomain adaptation will cause complex network structure with many source classes. To address these problems, this paper proposes a partial domain adaptation scheme based on weighted adversarial nets with improved convolutional block attention module (CBAM) for WT gearbox unsupervised fault diagnosis. In detail, a residual convolutional network combining the improved CBAM is designed to extract finer domain discriminative features for knowledge transfer. Meanwhile, a weighting mechanism based on the two-stage domain discriminator is designed to evaluate the contribution of each source sample, through which a simplified transfer network structure is constructed and the source samples unrelated to the target domain can be filtered. Furthermore, an adversarial transfer strategy is introduced to decrease the distribution discrepancy between domains, then the helpful diagnosis knowledge can be transferred. Experiments on two cases demonstrate the superiority and effectiveness of the proposed method compared with existing domain adaptation methods.

Exploring themes of racialization in “The Vanishing Half”: Is the term “white passing” a useful way for psychotherapists, counsellors and psychological therapists to conceptualise racial identity?

ABSTRACT This paper draws on themes from The Vanishing Half and intersectionality theory to evaluate the usefulness of the term ‘white passing’ for psychological therapists, counsellors and psychotherapists exploring racial identity phenomena in their practice. The context for ‘white passing’ as a historic method for surviving racial injustice is discussed. This will involve consideration of how the systematic invisibility of whiteness has been a cause for harmful racialization of individuals past and present. The conclusion is that ‘white passing’ as a flattened identity label contains dangerous rhetoric about how far racialized individuals are allowed to ‘fit’ into society by those that oppress them.

Partial Domain Adaptation Without Domain Alignment.

Unsupervised domain adaptation (UDA) aims to transfer knowledge from a well-labeled source domain to a related and unlabeled target domain with identical label space. The main workhorse in UDA is domain alignment and has proven successful. However, it is practically difficult to find an appropriate source domain with identical label space. A more practical scenario is partial domain adaptation (PDA) where the source label space subsumes the target one. Unfortunately, due to the non-identity between label spaces, it is extremely hard to obtain an ideal alignment, conversely, easier resulting in mode collapse and negative transfer. These motivate us to find a relatively simpler alternative to solve PDA. To achieve this, we first explore a theoretical analysis, which says that the target risk is bounded by both model smoothness and between-domain discrepancy. Then, we instantiate the model smoothness as an intra-domain structure preserving (IDSP) while giving up possibly riskier domain alignment. To our best knowledge, this is the first naive attempt for PDA without alignment. Finally, our empirical results on benchmarks demonstrate that IDSP is not only superior to the PDA SOTAs (e.g., ∼+10% on Cl → Rw and ∼+8% on Ar → Rw), but also complementary to domain alignment in the standard UDA.

Disclosure, minority stress, and mental health among bisexual, pansexual, and queer (bi+) adults: The roles of primary sexual identity and multiple sexual identity label use.

Bisexual people are at increased risk for anxiety and depression compared to heterosexual and gay/lesbian people, but little is known about people who use other labels to describe attractions to more than one gender (e.g., pansexual, queer; collectively "bi+"). In addition, some people use more than one label to describe their sexual orientation, but research has yet to examine whether using one versus multiple labels is associated with identity-related experiences or mental health. To address these gaps, we explored potential differences in disclosure, minority stress, and mental health among bi+ adults based on primary sexual identity and multiple label use. As part of a larger project, 669 bi+ adults completed an online survey. Primary sexual identities included bisexual (53.2%), pansexual (26.3%), and queer (20.5%), and 55.2% used multiple labels. Compared to bisexual participants, pansexual participants reported higher disclosure, discrimination from heterosexual people, and depression. Pansexual participants also reported higher anxiety and lower internalized binegativity, but these associations became non-significant after adjusting for demographics. Queer participants reported higher disclosure, discrimination from heterosexual people, and anxiety, but only the difference in disclosure remained significant in adjusting analyses. Finally, participants who used multiple labels reported higher disclosure and discrimination from heterosexual and gay/lesbian people, but only the difference in discrimination from gay/lesbian people remained significant in adjusted analyses. Findings highlight the heterogeneity of bi+ individuals and the importance of considering bisexual, pansexual, and queer individuals as unique groups as well as considering whether bi+ individuals use one or multiple sexual identity labels.