Representativeness Research Articles

Representation of Jews and Anti-Jewish Bias in 19th Century French Public Discourse: Distant and Close Reading.

We explore through the lens of distant reading the evolution of discourse on Jews in France during the XIX century. We analyze a large textual corpus including heterogeneous sources—literary works, periodicals, songs, essays, historical narratives—to trace how Jews are associated to different semantic domains, and how such associations shift over time. Our analysis deals with three key aspects of such changes: the overall transformation of embedding spaces, the trajectories of word associations, and the comparative projection of different religious groups over different, historically relevant semantic dimensions or streams of discourse. This allows to show changes in the association between words and semantic domains (referring e.g. to economic and moral behaviors), the evolution of stereotypes, and the dynamics of bias over a long time span characterized by major historical transformations. We suggest that the analysis of large textual corpora can be fruitfully used in a dialogue with more traditional close reading approaches—by pointing to opportunities of in-depth analyses that mobilize more qualitative approaches and a detailed inspection of the sources that distant reading inevitably tends to aggregate. We offer a short example of such a dialogue between different approaches in our discussion of the Second Empire transformations, where we mobilize the historian’s tools to start disentangling the complex interactions between changes in French society, the nature of sources, and representations of Jews. While our example is limited in scope, we foresee large potential payoffs in the cooperative interaction between distant and close reading.

Multi-label recognition of cancer-related lesions with clinical priors on white-light endoscopy

Deep learning-based computer-aided diagnosis techniques have demonstrated encouraging performance in endoscopic lesion identification and detection, and have reduced the rate of missed and false detections of disease during endoscopy. However, the interpretability of the model-based results has not been adequately addressed by existing methods. This phenomenon is directly manifested by a significant bias in the representation of feature localization. Good recognition models experience severe feature localization errors, particularly for lesions with subtle morphological features, and such unsatisfactory performance hinders the clinical deployment of models. To effectively alleviate this problem, we proposed a solution to optimize the localization bias in feature representations of cancer-related recognition models that is difficult to accurately label and identify in clinical practice. Optimization was performed in the training phase of the model through the proposed data augmentation method and auxiliary loss function based on clinical priors. The data augmentation method, called partial jigsaw, can “break” the spatial structure of lesion-independent image blocks and enrich the data feature space to decouple the interference of background features on the space and focus on fine-grained lesion features. The annotation-based auxiliary loss function used class activation maps for sample distribution correction and led the model to present localization representation converging on the gold standard annotation of visualization maps. The results show that with the improvement of our method, the precision of model recognition reached an average of 92.79%, an F1-score of 92.61%, and accuracy of 95.56% based on a dataset constructed from 23 hospitals. In addition, we quantified the evaluation representation of visualization feature maps. The improved model yielded significant offset correction results for visualized feature maps compared with the baseline model. The average visualization-weighted positive coverage improved from 51.85% to 83.76%. The proposed approach did not change the deployment capability and inference speed of the original model and can be incorporated into any state-of-the-art neural network. It also shows the potential to provide more accurate localization inference results and assist in clinical examinations during endoscopies.

Associative Learning and Representativeness

The representativeness heuristic constitutes constitutes a striking departure from Bayesian inference. According to a strong form of the heuristic, agents reverse a conditioning argument: for example inferring that a patient is more likely than not to have a disease, conditional on a positive test result. The correct inference is that a positive test result is more likely than not, conditional on disease. Recent research implicates representativeness in a wide range of financial market anomalies, with consequences for the real economy. However, the cognitive foundations of representativeness heuristic (RH) remain unknown. Here, we show that the RH emerges from a model of associative memory, leading to a cognitive foundation for the RH, and a means of integrating the RH into economic models involving decision-making under uncertainty.

Measurement and Representation Bias in Responses on Educational Level in Public Opinion Research

Measurement and Representation Bias in Responses on Educational Level in Public Opinion Research

Oficinas de ciência e tecnologia como estratégia pedagógica para a educação científica de estudantes do ensino fundamental

Abstract This study aims to analyze the efficiency of science workshops and thus investigate associated teaching methods used with elementary school students. We evaluated the performance of the 20 participants of a science workshop during the development of 24 science projects in the second half of 2019. Their score was submitted to statistical analysis, which pointed to significant differences throughout the period. We observed that students' performance peaked between the first and the last month of the project. Workshops generate knowledge that, in a spectral and capillary way, is shared with the visitors through the speeches of each participant. Therefore, biased representations and concepts give place to new possibilities of education through research, in a way to fight denialism, which has become entrenched in our society.

Gender Representation in Nepali Commercials

This paper explores gender representation in visual media, especially in commercials broadcasted through various Nepali visual media. A content analysis of twenty-one commercials was done underpinning gender and feminist perspectives to explore gender representation in them. The advertisement samples were selected among some popular brands and the products of everyday use. The findings suggested reinforcement of traditional gender roles and reproduction of gender hierarchies by depicting men and women in traditionally specified roles, appearances, and concepts. Cooking, washing, and childcare were regarded as female jobs. More men were depicted in occupational roles and females were projected in relational roles. This research is based on the claim that biased gender representations in media sustain gender bias attitudes on viewers thus a careful selection of gender-neutral or counter-stereotypical content should be promoted.

Category-Biased Neural Representations Form Spontaneously during Learning That Emphasizes Memory for Specific Instances.

Category learning, learning to sort a set of stimuli into categories or groups, can induce category biases in perception such that items in the same category are perceived as more similar than items from different categories. To what degree category bias develops when learning goals emphasize individuation of each stimulus and whether the bias emerges spontaneously during learning itself rather than in response to task demands is unclear. Here, we used functional MRI (fMRI) during encoding to test for category biases in neural representations of individual stimuli during learning. Human participants (males and females) encountered face-blend stimuli with unique first names and shared family names that indicated category membership. Participants were instructed to learn the full name for each face. Neural pattern classification and pattern similarity analyses were used to track category information in the brain. Results showed that stimulus category could be decoded during encoding across many frontal, parietal, and occipital regions. Furthermore, two stimuli from the same category were represented more similarly in the prefrontal cortex than two stimuli from different categories equated for physical similarity. These findings illustrate that a mere presence of category label can bias neural representations spontaneously during encoding to emphasize category-relevant information, even in the absence of explicit categorization demands and when category-irrelevant information remains relevant for task goals.SIGNIFICANCE STATEMENT Entities belonging to the same category are perceived as being more similar than entities belonging to different categories. Here, we show that neural representations highlighting category-relevant information form spontaneously during encoding. Notably, the presence of a category label led to neural category bias although participants focused on remembering individual stimuli and category-irrelevant stimulus features remained important for explicit task goals. These results may inform our understanding of bias in general and suggest that bias may emerge when category information is present even when one's explicit focus is on individuals.

Gender-sensitive word embeddings for healthcare.

To analyze gender bias in clinical trials, to design an algorithm that mitigates the effects of biases of gender representation on natural-language (NLP) systems trained on text drawn from clinical trials, and to evaluate its performance. We analyze gender bias in clinical trials described by 16772 PubMed abstracts (2008-2018). We present a method to augment word embeddings, the core building block of NLP-centric representations, by weighting abstracts by the number of women participants in the trial. We evaluate the resulting gender-sensitive embeddings performance on several clinical prediction tasks: comorbidity classification, hospital length of stay prediction, and intensive care unit (ICU) readmission prediction. For female patients, the gender-sensitive model area under the receiver-operator characteristic (AUROC) is 0.86 versus the baseline of 0.81 for comorbidity classification, mean absolute error 4.59 versus the baseline of 4.66 for length of stay prediction, and AUROC 0.69 versus 0.67 for ICU readmission. All results are statistically significant. Women have been underrepresented in clinical trials. Thus, using the broad clinical trials literature as training data for statistical language models could result in biased models, with deficits in knowledge about women. The method presented enables gender-sensitive use of publications as training data for word embeddings. In experiments, the gender-sensitive embeddings show better performance than baseline embeddings for the clinical tasks studied. The results highlight opportunities for recognizing and addressing gender and other representational biases in the clinical trials literature. Addressing representational biases in data for training NLP embeddings can lead to better results on downstream tasks for underrepresented populations.

The affect bias in the metaphorical representation of anticipated events

Abstract When talking about anticipated events, speakers can conceptualize them either as destinations towards which they are moving or as entities moving towards them, which correspond to the Ego- and the Time-moving metaphors, respectively (cf. ‘We are approaching Christmas’ and ‘Christmas is approaching’). Research in psycholinguistics has shown affective valence, i.e. whether the conceptualized event is perceived as positive or negative, to be one of the factors that modulate metaphor choice; positive anticipation is preferentially associated with Ego-moving expressions, whereas negative anticipation is predominantly associated with Time-moving metaphors. This paper sets out to test if the time-affect association surfaces in naturally-occurring language use when both metaphorical patterns are available. By focusing on the temporal usage of the verb approach, we provide linguistic evidence in favor of such an affective bias in time representations. In addition, the language data point to a semantic preference for a particular type of event (i.e., personal vs social) under each metaphorical pattern. We interpret this finding as preliminary evidence for a possible semantic bias in time representations to be further investigated.

Microenvironment drives cell state, plasticity, and drug response in pancreatic cancer

SUMMARYPrognostically relevant RNA expression states exist in pancreatic ductal adenocarcinoma (PDAC), but our understanding of their drivers, stability, and relationship to therapeutic response is limited. To examine these attributes systematically, we profiled metastatic biopsies and matched organoid models at single-cell resolution. In vivo, we identify a new intermediate PDAC transcriptional cell state and uncover distinct site- and state-specific tumor microenvironments (TMEs). Benchmarking models against this reference map, we reveal strong culture-specific biases in cancer cell transcriptional state representation driven by altered TME signals. We restore expression state heterogeneity by adding back in vivo-relevant factors and show plasticity in culture models. Further, we prove that non-genetic modulation of cell state can strongly influence drug responses, uncovering state-specific vulnerabilities. This work provides a broadly applicable framework for aligning cell states across in vivo and ex vivo settings, identifying drivers of transcriptional plasticity and manipulating cell state to target associated vulnerabilities.

The implications of digital visual media for human–nature relationships

The implications of digital visual media for human–nature relationships

Causal Knowledge as a Prerequisite for Interrogating Bias: Reflections on Hernán et al. 20 Years Later.

In their seminal 2002 paper, "Causal Knowledge as a Prerequisite for Confounding Evaluation: An Application to Birth Defects Epidemiology," Hernán et al. (Am J Epidemiol. 2002;155(2):176-184) emphasized the importance of using theory rather than data to guide confounding control, focusing on colliders as variables that share characteristics with confounders but whose control may actually introduce bias into analyses. In this commentary, we propose that the importance of this paper stems from the connection the authors made between nonexchangeability as the ultimate source of bias and structural representations of bias using directed acyclic graphs. This provided both a unified approach to conceptualizing bias and a means of distinguishing between different sources of bias, particularly confounding and selection bias. Drawing on examples from the paper, we also highlight unresolved questions about the relationship between collider bias, selection bias, and generalizability and argue that causal knowledge is a prerequisite not only for identifying confounders but also for developing any hypothesis about potential sources of bias.

Heuristic biases and investment decisions: multiple mediation mechanisms of risk tolerance and financial literacy—a survey at the Tanzania stock market

PurposeThe purpose of this research is to examine the effect of heuristic biases on investment decisions through multiple mediation mechanisms of risk tolerance and financial literacy in the Tanzanian stock market.Design/methodology/approachA sample of 316 individual investors in the Tanzanian stock market was obtained through questionnaires. The data were analyzed using structural equation modeling (SEM).FindingsThe findings show that financial literacy mediates insignificantly the effects of overconfidence, availability, anchoring and representativeness heuristics on investment decisions. Further, financial literacy does not influence the effect of risk tolerance and investment decisions. Risk tolerance is confirmed as a positive mediator of overconfidence, availability, anchoring and representativeness heuristics in investment decisions. Also, the study shows that overconfidence exerts a stronger influence on investment decisions, followed by availability, representativeness, risk tolerance, anchoring and financial literacy.Research limitations/implicationsThe study deals with real investors. Therefore, it uses fewer items to measure the constructs in order to avoid respondent bias. Further research could examine the effects of heuristic biases on investment decisions by adding or modifying the items of particular constructs and studying institutional investors.Practical implicationsThe findings can help individual investors to analyze and evaluate their behavior toward stock selection. Securities institutions can use this research to understand investors' behavior, evaluate future market trends and provide advice to the investors.Originality/valuePrevious studies have examined the impact of heuristics on the investment decisions of individual investors. The unique empirical analysis developed in this paper is that it examines the multiple mediation mechanisms of risk tolerance and financial literacy with respect to heuristic biases and investment decisions in the Tanzanian stock market.

GENDER REPRESENTATION ON THE TEXT OF ARTICLES IN THE NEWSPAPERS

Since March 2020, the Government has enforced Large-Scale Social Restrictions (PSBB) in various red zones to minimize the number of Covid-19 cases, to work from home or live at home might bring additional issues, such as violence against women, in the latest situation. During the COVID-19 pandemic, newspapers play a vital role in reporting occurrences of female assault. .This article focuses on examining how women are exposed to violence in Indonesia during the current pandemic of coronavirus (COVID-19) from Critical Discourse Analysis. This research aimed to discover the Critical Discourse Analysis of newspaper articles that deal with violence against women. The articles is qualitative research, reported based on content analysis techniques. This research contributes to the analysis of media representation of women by providing context-sensitive results of the phenomena that occurred in women during the COVID-19 pandemic. The findings reveal the stability of discursive structures over time, especially gender bias in women's media representation during the COVID-19 pandemic. Women are still affected by gender bias due to the patriarchal, social, and culture that exists in Indonesia. The newspapers can provide a clear picture of women. This research by the Critical Discourse Analysis investigates how the BBC.com news, VOAindonesia.com texts reveal their meaning, while the newspapers reported women's existence as marginalizing, the BBC.com news, VOAindonesia.com, were also the courage to raise women's image as marginalizing.

Representing Polar Questions

Although the linguistic properties of polar questions have been extensively studied, comparatively little is known about how polar questions are processed in real time. In this paper, we report on three eye-tracking experiments on the processing of positive and negative polar questions in English and French. Our results show that in the early stages, participants pay attention to both positive and negative states of affairs for both positive and negative questions. In the late stages, positive and certain negative polar questions were associated with a bias for the positive state, and this bias appears to be pragmatic in nature. We suggest that different biases in mental representations reflect the hearer’s reasoning about the speaker’s purposes of enquiry.

Uneven geographies in the various language editions of Wikipedia: the case of Ukrainian cities

The paper tackles the issue of uneven geographical representations on Wikipedia, the most visible and powerful user-generated encyclopaedia. In particular, it addresses language imbalances on Wikipedia with regard to geographical information and uneven spatial patterns of territory coverage on the different language versions in an attempt to verify expectations about the cultural factors that influence these imbalances and uneven spatial patterns. Ukraine is a promising case for testing the formulated expectations, as it has a large number of neighbouring countries, and most of them had political and cultural influence on its territory in the past. The volumes (word counts) of articles about the Ukrainian cities were analysed for seven language versions of Wikipedia, including the Ukrainian version and the versions of all bordering countries. The results show that historical geography is the strongest and central factor, and most of the key relic borders (former boundaries) can be traced. Ethnic composition appears to be another important factor, although weaker than the previous one. The role of the border factor is often unclear, but in some cases it definitely makes an impact and therefore cannot be completely ignored. Thus, the geographies of Wikipedia are not indifferent to the issues of ethnicity and geopolitics. The research calls into question the ability of modern Wikipedia to be a reliable and balanced source of geographical knowledge, as the described imbalances may create lopsided and biased geographical representations in people from different countries and nations.

Fifty Generations of Amitosis: Tracing Asymmetric Allele Segregation in Polyploid Cells with Single-Cell DNA Sequencing.

Amitosis is a widespread form of unbalanced nuclear division whose biomedical and evolutionary significance remain unclear. Traditionally, insights into the genetics of amitosis have been gleaned by assessing the rate of phenotypic assortment. Though powerful, this experimental approach relies on the availability of phenotypic markers. Leveraging Paramecium tetraurelia, a unicellular eukaryote with nuclear dualism and a highly polyploid somatic nucleus, we probe the limits of single-cell whole-genome sequencing to study the consequences of amitosis. To this end, we first evaluate the suitability of single-cell sequencing to study the AT-rich genome of P. tetraurelia, focusing on common sources of genome representation bias. We then asked: can alternative rearrangements of a given locus eventually assort after a number of amitotic divisions? To address this question, we track somatic assortment of developmentally acquired Internal Eliminated Sequences (IESs) up to 50 amitotic divisions post self-fertilization. To further strengthen our observations, we contrast empirical estimates of IES retention levels with in silico predictions obtained through mathematical modeling. In agreement with theoretical expectations, our empirical findings are consistent with a mild increase in variation of IES retention levels across successive amitotic divisions of the macronucleus. The modest levels of somatic assortment in P. tetraurelia suggest that IESs retention levels are largely sculpted at the time of macronuclear development, and remain fairly stable during vegetative growth. In forgoing the requirement for phenotypic assortment, our approach can be applied to a wide variety of amitotic species and could facilitate the identification of environmental and genetic factors affecting amitosis.

Are we enrolling representative cohorts of premature infants in our clinical trials?

To compare the difference in outcomes in a subset population of infants "eligible but not enrolled; ENE" vs those who were "eligible and enrolled, EE" in The Australian Placental Transfusion Study (APTS). Population-based multicentre retrospective cohort study. A total of 535 (17.7%) infants were categorized as EE and 2489 (82.3%) ENE. ENE infants were significantly more premature (mean gestation 27.0 vs 28.0 weeks) but otherwise of similar anthropometric measures compared to EE infants. ENE infants had significantly higher incidences of low Apgar scores <7 at 5 min, CLD, IVH and PDA requiring treatment. Using a multivariate adjusted-analysis, ENE were at a greater risk for mortality (OR 1.86; 95% CI, 1.30-2.67, p < 0.001). Antenatal consenting may lead to biased population representation, which may affect trial results' generalizability. Retrospective consent or waiver of consent may improve the generalizability of neonatal and emergency clinical trials.

Southern Ocean polynyas in CMIP6 models

Abstract. Polynyas facilitate air–sea fluxes, impacting climate-relevant properties such as sea ice formation and deep water production. Despite their importance, polynyas have been poorly represented in past generations of climate models. Here we present a method to track the presence, frequency and spatial distribution of polynyas in the Southern Ocean in 27 models participating in the Climate Model Intercomparison Project Phase 6 (CMIP6) and two satellite-based sea ice products. Only half of the 27 models form open-water polynyas (OWPs), and most underestimate their area. As in satellite observations, three models show episodes of high OWP activity separated by decades of no OWP, while other models unrealistically create OWPs nearly every year. In contrast, the coastal polynya area is overestimated in most models, with the least accurate representations occurring in the models with the coarsest horizontal resolution. We show that the presence or absence of OWPs is linked to changes in the regional hydrography, specifically the linkages between polynya activity with deep water convection and/or the shoaling of the upper water column thermocline. Models with an accurate Antarctic Circumpolar Current transport and wind stress curl have too frequent OWPs. Biases in polynya representation continue to exist in climate models, which has an impact on the regional ocean circulation and ventilation that should be addressed. However, emerging iceberg discharge schemes, more adequate vertical grid type or overflow parameterisation are anticipated to improve polynya representations and associated climate prediction in the future.

Eliminating Performance Gaps through Corporate Governance for Sustainable Management of Selected Manufacturing Companies in Calabar, Cross River State, Nigeria

The study on eliminating performance gaps through corporate governance for sustainable management was conducted in selected manufacturing companies in Cross River State. The study examined transparency in financial disclosure and accountability in managerial ownership as measures for sustainable management in a dynamic business environment. Two research questions and hypotheses were respectively formulated and agency theory was developed to guide the study. Survey research design was adopted and with a population of 240, a sample size of 152 staff determined with the use of Taro Yamane sampling formula. Stratified sampling method and Bowley’s proportional allocation formular was adopted with the aim of preventing bias in representation. A 21-item structured questionnaire facilitated the collection of data and a pilot study was carried out to determine potential weakness in the research instrument. The instrument was developed, validated and tested for reliability using test-retest method. Data were analyzed using Data and simple percentage andhistogram to show Normality while regression analysis was used to test the hypotheses using SPSS. Finding shows that corporate governance implementation requires transparent financial disclosure to eliminate performance gaps and improve internal efficiency of the organizations. Priority on transparent financial information is addressed by corporate governance and this prevents fraud or irregularities in accounting for improved financial disclosures. The implication of the finding was the rejection of null hypotheses. The study concludes that performance gaps are eliminated by corporate governance principles which aid in realization of stakeholders’ expectation. The study recommends among others that management of the studied organizations should continuously eliminate performance gaps through compliance with International Financial Standards for good practices in corporate governance. Manager’s activities should be continuously monitored by board of directors in order to eliminate performance gaps.