A gender recognition method based on EEG microstates.

BACKGROUND: Gender carries important information related to male and female characteristics, and a large number of studies have attempted to use physiological measurement methods for gender classification. Although previous studies have shown that there exist statistical differences in some Electroencephalographic (EEG) microstate parameters between males and females, it is still unknown that whether these microstate parameters can be used as potential biomarkers for gender classification based on machine learning. METHODS: We used two independent resting-state EEG datasets: the first dataset included 74 females and matched 74 males, and the second one included 42 males and matched 42 females. EEG microstate analysis based on modified k-means clustering method was applied, and temporal parameter and nonlinear characteristics (sample entropy and Lempel-Ziv complexity) of EEG microstate sequences were extracted to compare between males and females. More importantly, these microstate temporal parameters and complexity were tried to train six machine learning methods for gender classification. RESULTS: We obtained five common microstates for each dataset and each group. Compared with the male group, the female group has significantly higher temporal parameters of microstate B, C, E and lower temporal parameters of microstate A and D, and higher complexity of microstate sequence. When using combination of microstate temporal parameters and complexity or only microstate temporal parameters as classification features in an independent test set (the second dataset), we achieved 95.2% classification accuracy. CONCLUSION: Our research findings indicate that the dynamics of microstate have considerable Gender-specific alteration. EEG microstates can be used as neurophysiological biomarkers for gender classification.

Task-related modulation of motor response to emotional bodies: A TMS motor-evoked potential study.

Exposure to emotional body postures during perceptual decision-making tasks has been linked to transient suppression of motor reactivity, supporting the monitoring of emotionally relevant information. However, it remains unclear whether this effect occurs implicitly, i.e., when emotional information is irrelevant to the task. To investigate this issue, we used single-pulse transcranial magnetic stimulation (TMS) to assess motor excitability while healthy participants were asked to categorize pictures of body expressions as emotional or neutral (emotion recognition task) or as belonging to a male or a female actor (gender recognition task) while receiving TMS over the motor cortex at 100 and 125 ms after picture onset. Results demonstrated that motor-evoked potentials (MEPs) were reduced for emotional body postures relative to neutral postures during the emotion recognition task. Conversely, MEPs increased for emotional body postures relative to neutral postures during the gender recognition task. These findings indicate that motor inhibition, contingent upon observing emotional body postures, is selectively associated with actively monitoring emotional features. In contrast, observing emotional body postures prompts motor facilitation when task-relevant features are non-emotional. These findings contribute to embodied cognition models that link emotion perception and action tendencies.

Gender Recognition Based on Gradual and Ensemble Learning from Multi-View Gait Energy Images and Poses.

Image-based gender classification is very useful in many applications, such as intelligent surveillance, micromarketing, etc. One common approach is to adopt a machine learning algorithm to recognize the gender class of the captured subject based on spatio-temporal gait features extracted from the image. The image input can be generated from the video of the walking cycle, e.g., gait energy image (GEI). Recognition accuracy depends on the similarity of intra-class GEIs, as well as the dissimilarity of inter-class GEIs. However, we observe that, at some viewing angles, the GEIs of both gender classes are very similar. Moreover, the GEI does not exhibit a clear appearance of posture. We postulate that distinctive postures of the walking cycle can provide additional and valuable information for gender classification. This paper proposes a gender classification framework that exploits multiple inputs of the GEI and the characteristic poses of the walking cycle. The proposed framework is a cascade network that is capable of gradually learning the gait features from images acquired in multiple views. The cascade network contains a feature extractor and gender classifier. The multi-stream feature extractor network is trained to extract features from the multiple input images. Features are then fed to the classifier network, which is trained with ensemble learning. We evaluate and compare the performance of our proposed framework with state-of-the-art gait-based gender classification methods on benchmark datasets. The proposed framework outperforms other methods that only utilize a single input of the GEI or pose.

Automatic gender and unilateral load state recognition for biometric purposes.

BACKGROUND: Automatic recognition of a person's gender as well as his or her unilateral load state are issues that are often analyzed and utilized by a wide range of applications. For years, scientists have recognized human gait patterns for purposes connected to medical diagnoses, rehabilitation, sport, or biometrics. OBJECTIVE: The present paper makes use of ground reaction forces (GRF) generated during human gait to recognize gender or the unilateral load state of a walking person as well as the combination of both of those characteristics. METHODS: To solve the above-stated problem parameters calculated on the basis of all GRF components such as mean, variance, standard deviation of data, peak-to-peak amplitude, skewness, kurtosis, and Hurst exponent as well as leading classification algorithms including kNN, artificial neural networks, decision trees, and random forests, were utilized. Data were collected by means of Kistler's force plates during a study carried out at the Bialystok University of Technology on a sample of 214 people with a total of 7,316 recorded gait cycles. RESULTS: The best results were obtained with the use of the kNN classifier which recognized the gender of the participant with an accuracy of 99.37%, the unilateral load state with an accuracy reaching 95.74%, and the combination of those two states with an accuracy of 95.31% which, when compared to results achieved by other authors are some of the most accurate. CONCLUSION: The study has shown that the given set of parameters in combination with the kNN classifying algorithm allows for an effective automatic recognition of a person's gender as well as the presence of an asymmetrical load in the form of a hand-carried briefcase. The presented method can be used as a first stage in biometrics systems.

Twitter-based gender recognition using transformers.

Social media contains useful information about people and society that could help advance research in many different areas of health (e.g. by applying opinion mining, emotion/sentiment analysis and statistical analysis) such as mental health, health surveillance, socio-economic inequality and gender vulnerability. User demographics provide rich information that could help study the subject further. However, user demographics such as gender are considered private and are not freely available. In this study, we propose a model based on transformers to predict the user's gender from their images and tweets. The image-based classification model is trained in two different methods: using the profile image of the user and using various image contents posted by the user on Twitter. For the first method a Twitter gender recognition dataset, publicly available on Kaggle and for the second method the PAN-18 dataset is used. Several transformer models, i.e. vision transformers (ViT), LeViT and Swin Transformer are fine-tuned for both of the image datasets and then compared. Next, different transformer models, namely, bidirectional encoders representations from transformers (BERT), RoBERTa and ELECTRA are fine-tuned to recognize the user's gender by their tweets. This is highly beneficial, because not all users provide an image that indicates their gender. The gender of such users could be detected from their tweets. The significance of the image and text classification models were evaluated using the Mann-Whitney U test. Finally, the combination model improved the accuracy of image and text classification models by 11.73 and 5.26% for the Kaggle dataset and by 8.55 and 9.8% for the PAN-18 dataset, respectively. This shows that the image and text classification models are capable of complementing each other by providing additional information to one another. Our overall multimodal method has an accuracy of 88.11% for the Kaggle and 89.24% for the PAN-18 dataset and outperforms state-of-the-art models. Our work benefits research that critically require user demographic information such as gender to further analyze and study social media content for health-related issues.

Semi-supervised approach to identify steps, shoes, and gender of older adults under semi-naturalistic conditions using a waist-worn inertial sensor.

An emerging source of information to recognize individuals' characteristics are the walking pattern-related parameters. The elderly can be one of the populations that can benefit most from recognition-based applications, which may help to increase their possibilities of living independently at home. Approaches have been mostly focused on gait events' identification or assessment; nonetheless, such information can also be used to obtain seniors' characteristics that depend on physiological or environmental factors. These factors can be useful to provide a customized assistance based on contextual information. In this paper, we propose a method focused on seniors, to detect steps, and to recognize gender and type of shoes by using only the initial foot contact (IC) data obtained from inertial sensors during semi-controlled walking. Data were collected from 20 older adults who walked at self-speed in a natural environment. The method consists of first clustering the IC using k-means; then, a trained recurrent neural network recognizes gender, type of shoes, and the step phases (IC and other phases); to finally conduct step detection (SD) using a ruled-based method. The method recognizes gender and the type of shoes with an accuracy of 93% and 83.07%, respectively, whereas there were not misrecognitions of the step phases. SD achieved a mean absolute percentage error equal to 0.64%. The good results show that the method is appropriate for users' characteristics recognition applications without depending on assumptions based on individualities. Likewise, the method can be useful to monitor physical activity or systems aimed to keep safe older adults.

Boosting Archimedes optimization algorithm using trigonometric operators based on feature selection for facial analysis.

Due to technical advancements and the proliferation of mobile applications, facial analysis (FA) of humans has recently become an important area for computer vision research. FA investigates a variety of difficulties, including gender recognition, facial expression recognition, age and race recognition, with the goal of automatically comprehending social interactions. Due to the dimensional challenge posed by pre-trained CNN networks, the scientific community has developed numerous techniques inspired by biology, swarm intelligence theory, physics, and mathematical rules. This article presents a gender recognition system based on scAOA, that is a modified version of the Archimedes optimization algorithm (AOA). The latest variant (scAOA) enhances the exploitation stage by using trigonometric operators inspired by the sine cosine algorithm (SCA) in order to prevent local optima and to accelerate the convergence. The main purpose of this paper is to apply scAOA to select the relevant deep features provided by two pretrained models of CNN (AlexNet & ResNet) to recognize the gender of a human person categorized into two classes (men and women). Two datasets are used to evaluate the proposed approach (scAOA): the Brazilian FEI dataset and the Georgia Tech Face dataset (GT). In terms of accuracy, Fscore and statistical test, the comparison analysis demonstrates that scAOA outperforms other modern and competitive optimizers such as AOA, SCA, Ant lion optimizer (ALO), Salp swarm algorithm (SSA), Grey wolf optimizer (GWO), Simple genetic algorithm (SGA), Grasshopper optimization algorithm (GOA) and Particle swarm optimizer (PSO).

Identity and reproductive rights of transgenders: are current legal and ethical frameworks soon to be outdated? Medicolegal implications of potentially life-changing decisions.

Abstract: The level of recognition that transgender individuals (i.e. those whose gender does not match the sex assigned at birth) enjoy in our societies has certainly made giant strides. Still, there is no denying that the far-reaching ramifications arising from choices about one's gender expression do affect vital aspects of identity in school, workplaces, and the community, and should be clearly defined and addressed by laws and policies. One of the arguments most commonly used by supporters of transgender rights relies on the concept of inalienable human rights, including the rights to live safely, freely, and without fearing discrimination. The authors have set out to succinctly outline and elaborate on the dynamics that have been shaping the legal reco-gnition of transgender individuals in light of the unique legal, social and ethical complexities that such an evolution entails. Moreover, as assisted reproduction technologies make considerable progress and innovations open up new horizons for fertility preservation and restoration, it is worth exploring how such advance can play a role in upholding the reproductive rights of transgender patients who wish to achieve parenthood, and how counseling ought to be implemented taking into account the psychological traits of transgender patients and the implications of every choice they make.

Heterogeneity in the Desire to Undergo Various Gender-Affirming Medical Interventions Among Transgender People in Hong Kong: Findings from a Community-Driven Survey and Implications for the Legal Gender Recognition Debate.

Previous estimates suggest that there are at least nine million transgender people in Asia-Pacific; however, in most countries, legal gender recognition has not been made possible or there are otherwise stringent eligibility criteria. The obligation of having undergone gender-affirming medical interventions as a basis for such recognition is being hotly debated. However, there has been little empirical evidence on the desire to undergo various gender-affirming medical interventions among transgender people. This study fills the research gap by studying Hong Kong, where a transgender person must produce medical evidence for "complete" sex reassignment surgery in order to change the sex entry on their identity card. A community-driven survey of 234 transgender people found that only 13.0% of the participants who were assigned male at birth could fit such a requirement. Strikingly, because none of the participants assigned female at birth had undergone construction of a penis or some form of a penis, all of them would be excluded from legal gender recognition. Financial reasons and reservations about surgical risks and/or techniques were the most commonly cited reasons for not undertaking the medical interventions. The findings suggest that an overwhelming majority of transgender people in Hong Kong are excluded from legal gender recognition, which fundamentally affects their civil, political, economic, social, and cultural rights. More generally, this study shows heterogeneity among transgender people in the desire for different gender-affirming medical interventions, and thus argues that the legal gender recognition debate needs to consider their concerns and self-determination.

Barriers to Possessing Gender-Concordant Identity Documents are Associated with Transgender and Nonbinary People's Mental Health in Aotearoa/New Zealand.

Purpose: This study sought to expand on previous scholarship focused on gender-concordant identity documents (IDs) as a social determinant of health. We examined the association between barriers to legal gender recognition and the mental health of transgender and nonbinary people in Aotearoa/New Zealand. Methods: We used data from a 2018 nationwide community-based survey of trans and nonbinary people in Aotearoa (N = 818). Variables of investigation included: gender-concordant IDs, mental health (past-month psychological distress, past-year nonsuicidal self-injury, past-year suicidality) and barriers to changing gender markers on a birth certificate or passport. Associations between gender-concordant IDs and mental health were determined using generalized linear regression models. Results: In total, 34.8% reported the correct name on all of their IDs. The proportion with the correct gender marker on both birth certificates and passports was 16.0%. Participants with gender-concordant IDs were more likely to be older, have higher levels of income and education, and have had genital reconstruction. In addition, 68.7% of participants reported experiencing at least one barrier to changing gender markers on their IDs, and these participants had significantly higher average points of psychological distress scores (b = 2.39) and greater odds of suicidal ideation (odds ratio = 2.02) than those with gender-concordant IDs, after adjusting for sociodemographic variables. Conclusion: We present novel findings on higher levels of mental health problems among trans and nonbinary people who faced barriers in trying to obtain gender-concordant IDs compared with those with gender-concordant IDs. Removing barriers to legal gender recognition may be an effective way to improve mental health.

FCML-gait: fog computing and machine learning inspired human identity and gender recognition using gait sequences.

Security threats are always there if the human intruders are not identified and recognized well in time in highly security-sensitive environments like the military, airports, parliament houses, and banks. Fog computing and machine learning algorithms on Gait sequences can prove to be better for restricting intruders promptly. Gait recognition provides the ability to observe an individual unobtrusively, without any direct cooperation or interaction from the people, making it very attractive than other biometric recognition techniques. In this paper, a Fog Computing and Machine Learning Inspired Human Identity and Gender Recognition using Gait Sequences (FCML-Gait) are proposed. Internet of things (IoT) devices and video capturing sensors are used to acquire data. Frames are clustered using the affinity propagation (AP) clustering technique into several clusters, and cluster-based averaged gait image(C-AGI) feature is determined for each cluster. For training and testing of datasets, sparse reconstruction-based metric learning (SRML) and Speeded Up Robust Features (SURF) with support vector machine (SVM) are applied on benchmark gait database ADSC-AWD having 80 subjects of 20 different individuals in the Fog Layer to improve the processing. The performance metrics, for instance, accuracy, precision, recall, F-measure, C-time, and R-time have been measured, and a comparative evaluation of the projected method with the existing SRML technique has been provided in which the proposed FCML-Gait outperforms and attains the highest accuracy of 95.49%.

Gender Identification in a Two-Level Hierarchical Speech Emotion Recognition System for an Italian Social Robot.

The real challenge in Human-Robot Interaction (HRI) is to build machines capable of perceiving human emotions so that robots can interact with humans in a proper manner. Emotion varies accordingly to many factors, and gender represents one of the most influential ones: an appropriate gender-dependent emotion recognition system is recommended indeed. In this article, we propose a Gender Recognition (GR) module for the gender identification of the speaker, as a preliminary step for the final development of a Speech Emotion Recognition (SER) system. The system was designed to be installed on social robots for hospitalized and living at home patients monitoring. Hence, the importance of reducing the software computational effort of the architecture also minimizing the hardware bulkiness, in order for the system to be suitable for social robots. The algorithm was executed on the Raspberry Pi hardware. For the training, the Italian emotional database EMOVO was used. Results show a GR accuracy value of 97.8%, comparable with the ones found in the literature.

Bogazici university smartphone accelerometer sensor dataset.

Mobile devices especially smartphones have gained high popularity and become a part of daily life in recent years. Therefore, there are many studies that investigate users' interactions with smartphones and try to extract meaningful information from various inputs. Actually, the main motivation behind these studies is the behavioral differences of users in their interactions with smartphones. In these studies, motion sensors in devices such as accelerometer and gyroscope are widely used. Data obtained from motion sensors allows to detect information such as age-group, gender, activity type, identity of users. In this context, we develop an Android application that gathers accelerometer sensor data while users perform different activities. This application records all accelerometer data and touch event information generated while users are using their devices. Then, we perform two experiments and collect two different data using this application. In the first experiment, we collect data from 107 child users and 100 adult users to analyze the impact of different age-groups' behavior on sensor data. This dataset includes more than 11.000 taps data for child and adult users, in total. In the second experiment, data is collected from 60 female and 60 male users for different activities like sitting and walking. There are more than 6.000 taps data for sitting and walking scenarios separately in the second dataset. This dataset makes it possible to analyze the changes created by different gender and activity types in the sensor data. These data can be used for behavioral biometric analyses on smartphones such as user age-group and gender detection, user identification and authentication.

Undergoing pregnancy and childbirth as trans masculine in Sweden: experiencing and dealing with structural discrimination, gender norms and microaggressions in antenatal care, delivery and gender clinics.

BACKGROUND: A sterilization requirement to change legal gender was removed from Swedish law in 2013, facilitating pregnancy in trans masculine individuals. The limited number of studies investigating pregnancy and childbirth among trans masculine individuals indicate increased gender dysphoria and negative experiences of pre- and post-natal healthcare, highlighting a need to improve care. Research focusing on Europe or contexts where sterilization to change legal gender was previously required by national law remains minimal. AIMS: This study aimed to investigate how trans masculine individuals experience healthcare encounters in connection with pregnancy, delivery and nursing, in a setting where mandatory sterilization to change legal gender was recently removed. METHODS: In-depth face-to-face interviews were conducted with 12 trans masculine individuals who attended Swedish prenatal care and delivered a child after the law on legal gender recognition was amended. Thematic content analysis was used. RESULTS: Providers in gender clinics, antenatal care and delivery were perceived to regard a masculine gender identity and pregnancy as incompatible. The main categories encompassed expectations and experiences of pregnancy related care and participant responses to it. Participants took charge of their care to ensure that their needs were fulfilled. The quality of care was inconsistent. DISCUSSION: A lack of knowledge, narrow gender norms and the legacy of the former legal sterility requirement limited access to diagnostic evaluation of gender dysphoria, information on reproduction and gender-affirming treatment. Medical safety during pregnancy, childbirth and nursing was impeded, gender dysphoria increased, and participants experienced minority stress. Attempts to avoid microaggressions guided healthcare encounters and birth wishes. Navigating healthcare required considerable attention, personal resources and energy, leaving particularly vulnerable individuals at risk of a lower quality of care. The paper concludes with clinical recommendations.

Age and Gender Recognition Using a Convolutional Neural Network with a Specially Designed Multi-Attention Module through Speech Spectrograms.

Speech signals are being used as a primary input source in human-computer interaction (HCI) to develop several applications, such as automatic speech recognition (ASR), speech emotion recognition (SER), gender, and age recognition. Classifying speakers according to their age and gender is a challenging task in speech processing owing to the disability of the current methods of extracting salient high-level speech features and classification models. To address these problems, we introduce a novel end-to-end age and gender recognition convolutional neural network (CNN) with a specially designed multi-attention module (MAM) from speech signals. Our proposed model uses MAM to extract spatial and temporal salient features from the input data effectively. The MAM mechanism uses a rectangular shape filter as a kernel in convolution layers and comprises two separate time and frequency attention mechanisms. The time attention branch learns to detect temporal cues, whereas the frequency attention module extracts the most relevant features to the target by focusing on the spatial frequency features. The combination of the two extracted spatial and temporal features complements one another and provide high performance in terms of age and gender classification. The proposed age and gender classification system was tested using the Common Voice and locally developed Korean speech recognition datasets. Our suggested model achieved 96%, 73%, and 76% accuracy scores for gender, age, and age-gender classification, respectively, using the Common Voice dataset. The Korean speech recognition dataset results were 97%, 97%, and 90% for gender, age, and age-gender recognition, respectively. The prediction performance of our proposed model, which was obtained in the experiments, demonstrated the superiority and robustness of the tasks regarding age, gender, and age-gender recognition from speech signals.

Authentication through gender classification from iris images using support vector machine.

Soft biometric information, such as gender, iris, and voice, can be helpful in various applications, such as security, authentication, and validation. Iris is secure biometrics with low forgery and error rates due to its highly certain features are being used in the last few decades. Iris recognition could be used both independently and in part for secure recognition and authentication systems. Existing iris-based gender classification techniques have low accuracy rates as well as high computational complexity. Accordingly, this paper presents an authentication approach through gender classification from iris images using support vector machine (SVM) that has an excellent response to sustained changes using the Zernike, Legendre invariant moments, and Gradient-oriented histogram. In this study, invariant moments are used as feature extraction from iris images. After extracting these descriptors' attributes, the attributes are categorized through keycode fusion. SVM is employed for gender classification using a fused feature vector. The proposed approach is evaluated on the CVBL data set and results are compared in state of the art based on local binary patterns and Gabor filters. The proposed approach came out with 98% gender classification rate with low computational complexity that could be used as an authentication measure.

A Comparison of an Artificial Intelligence Tool to Fundamental Frequency as an Outcome Measure in People Seeking a More Feminine Voice.

OBJECTIVES/HYPOTHESIS: An artificial intelligence (AI) tool was developed using audio clips of cis-male and cis-female voices based on spectral analysis to assess %probability of a voice being perceived as female (%Prob♀). This program was validated with 92% accuracy in cisgender speakers. The aim of the study was to assess the relationship of fo on %Prob♀ by a validated AI tool in a cohort of trans females who underwent intervention to feminize their voice with behavioral modification and/or surgery. STUDY DESIGN: Cohort study. METHODS: Fundamental frequency (fo ) from prolonged vowel sounds (fo /a/) and fo from spontaneous speech (fo -sp) were measured using the Kay Pentax Computerized Speech Lab (Montvale, NJ) in trans females postintervention. The same voice samples were analyzed by the AI tool for %Prob♀. Chi-square analysis and regression models were performed accepting >50% Prob♀ as female voice. RESULTS: Forty-two patients were available for analysis after intervention. fo -sp post-treatment was positively correlated with %Prob♀ (R = 0.645 [P < .001]). Chi-square analysis showed a significant association between AI %Prob♀ >50% for the speech samples and fo -sp >160 Hz (P < .01). Sixteen of 42 patients reached an fo -sp >160 Hz. Of these, the AI program only perceived nine patients as female (>50 %Prob♀). CONCLUSION: Patients with fo -sp >160 Hz after feminization treatments are not necessarily perceived as having a high probability of being female by a validated AI tool. AI may represent a useful outcome measurement tool for patients undergoing gender affirming voice care. LEVEL OF EVIDENCE: 3 Laryngoscope, 131:2567-2571, 2021.

Efficient facial representations for age, gender and identity recognition in organizing photo albums using multi-output ConvNet.

This paper is focused on the automatic extraction of persons and their attributes (gender, year of born) from album of photos and videos. A two-stage approach is proposed in which, firstly, the convolutional neural network simultaneously predicts age/gender from all photos and additionally extracts facial representations suitable for face identification. Here the MobileNet is modified and is preliminarily trained to perform face recognition in order to additionally recognize age and gender. The age is estimated as the expected value of top predictions in the neural network. In the second stage of the proposed approach, extracted faces are grouped using hierarchical agglomerative clustering techniques. The birth year and gender of a person in each cluster are estimated using aggregation of predictions for individual photos. The proposed approach is implemented in an Android mobile application. It is experimentally demonstrated that the quality of facial clustering for the developed network is competitive with the state-of-the-art results achieved by deep neural networks, though implementation of the proposed approach is much computationally cheaper. Moreover, this approach is characterized by more accurate age/gender recognition when compared to the publicly available models.

Trans Parenthood and the Meaning of 'Mother', 'Father' and 'Parent'-R (McConnell and YY) v Registrar General for England and Wales [2020] EWCA Civ 559.

In R (McConnell and YY) v Registrar General for England and Wales [2020] EWCA Civ 559, the Court of Appeal held the Registrar General was correct to register a trans man, who had given birth after the issuing of his gender recognition certificate, as 'mother' on his son's birth certificate. In their judgement, the court rejected the appellants' contention that the Gender Recognition Act 2004 should be construed to allow registration as either 'father' or 'parent'. The court further held that the interference with the appellants' Article 8 rights which resulted from the registration as 'mother' was proportionate and justified.

Reflections on Gender Analyses of Bibliographic Corpora.

The interplay between an academic's gender and their scholarly output is a riveting topic at the intersection of scientometrics, data science, gender studies, and sociology. Its effects can be studied to analyze the role of gender in research productivity, tenure and promotion standards, collaboration and networks, or scientific impact, among others. The typical methodology in this field of research is based on a number of assumptions that are customarily not discussed in detail in the relevant literature, but undoubtedly merit a critical examination. Presumably the most confronting aspect is the categorization of gender. An author's gender is typically inferred from their name, further reduced to a binary feature by an algorithmic procedure. This and subsequent data processing steps introduce biases whose effects are hard to estimate. In this report we describe said problems and discuss the reception and interplay of this line of research within the field. We also outline the effect of obstacles, such as non-availability of data and code for transparent communication. Building on our research on gender effects on scientific publications, we challenge the prevailing methodology in the field and offer a critical reflection on some of its flaws and pitfalls. Our observations are meant to open up the discussion around the need and feasibility of more elaborated approaches to tackle gender in conjunction with analyses of bibliographic sources.