Persistent Bias Research Articles

Mean State of the Northern Hemisphere Stratospheric Polar Vortex in Three Generations of CMIP Models

Abstract Progress and persistent biases in the simulation of the stratospheric polar vortex from three generations of Coupled Model Intercomparison Projects (CMIPs) are assessed. On average, the stratospheric cold bias is largest in CMIP3, but is improved in CMIP5 and CMIP6. The climatological ridge in the stratosphere over the North Pacific is underestimated persistently across CMIPs. Four parameters of the stratospheric polar vortex are evaluated among models and among CMIPs, and some common biases are identified for most models, including the too-large size of the vortex periphery, overstrong strength, too-small aspect ratio of the vortex shape, and too-westward displaced a vortex centroid. Intermodel spread in some parameters is highly correlated with the sea surface temperature (SST) bias in northern tropical oceans. Namely, a cold SST bias in the northern tropical Pacific is associated with a too-strong and too-large polar vortex, and a cold SST bias in the northern tropical Atlantic seems to be related to the westward bias of the vortex centroid. The implications of biases in the mean state of the vortex for stratospheric events such as sudden stratospheric warmings (SSWs) are also identified in this study. Models with a climatological vortex that is too strong, has too low an aspect ratio, or has a westward-biased centroid usually produce fewer SSWs, whereas ones with weaker strength, high aspect ratio, and eastward-biased centroid produce more. Based on the multimodel mean, the biases of all parameters have improved across three generations of CMIPs, although persistent biases in some individual models exist across CMIPs. Intermodel relationships between the polar vortex and SST highlights the important role of the SST simulation for the stratosphere.

Genus-level, trait-based multimetric diatom indices for assessing the ecological condition of rivers and streams across the conterminous United States

Taxonomic inconsistency in species-level identifications has constrained use of diatoms as biological indicators in aquatic assessments. We addressed this problem by developing diatom multimetric indices (MMIs) of ecological condition using genus-level taxonomy and trait-based autecological information. The MMIs were designed to assess river and stream chemical, physical and biological condition across the conterminous United States. Trait-based approaches have the advantage of using both species-level and genus-level data, which require less effort and expense to acquire than traditional species-based approaches and eliminate the persistent taxonomic biases introduced over vast geographic extents. For large-extent assessment programs that require multiple taxonomic laboratories to process samples, such as the United States Environmental Protection Agency’s (U.S. EPA’s) National Rivers and Streams Assessment (NRSA), the trait approach can eliminate discrepancies in species-level identification or nomenclature that hinder diatom data interpretation. We developed trait-based MMIs using NRSA data for each of the three large ecoregions across the U.S. - the East, Plains, and West. All three MMIs performed well in discriminating least-disturbed from most-disturbed sites. The MMI for the East had the greatest discrimination ability, followed by MMIs for the Plains and West, respectively. The performance of the MMIs was comparable to that observed in existing NRSA fish and macroinvertebrate MMIs. Our research shows that trait-based diatom indices constructed on genus-level taxonomy can be effective for large-scale assessments, and may also allow programs such as NRSA to assess trends in freshwater condition retrospectively, by revisiting older diatom datasets. Moreover, our genus-based approach facilitates including of diatoms into other assessment programs that have limited monitoring resources.

Improved Surface Mass Balance Closure in Ocean Hindcast Simulations

AbstractForced global ocean/sea‐ice hindcast simulations are subject to persistent surface mass flux estimation biases, for example, configurations with an explicit‐free surface may not take into account the seasonal storage of water on land when constraining sea level. We present a physically motivated surface mass flux closure, that results in: reduced watermass drift from initialization; improved Atlantic meridional overturning cirulation intensity; and more realistic rates of ocean heat uptake, in simulations using global ocean/sea‐ice/land (MOM6/SIS2/LM3) model configurations, forced with atmospheric reanalysis data. In addition to accounting for the land storage, the area‐integrated subpolar‐to‐polar (40°–90°N/S) surface mass fluxes are constrained, using a climatological estimate derived from the the CMIP6 historical ensemble, which helps to further improve hindcast performance. Simulations using MERRA‐2 and JRA55‐do forcing, subject to identical hydrologic constraints, exhibit similar reductions in drift.

Improvements and persistent biases in the southeast tropical Atlantic in CMIP models

State-of-the-art climate models simulate warmer than observed sea surface temperatures (SST) in eastern boundary upwelling systems (EBUS), generating biases with profound implications for the simulation of present-day climate and its future projections. Amongst all EBUS, the bias is largest in the southeastern tropical Atlantic (SETA). Here, we provide a comprehensive evaluation of the performance in the SETA of the Coupled Model Intercomparison Project phase 6 (CMIP6), including fine resolution (HighResMIP) and ocean-forced (OMIP) models. We show that biases in the SETA remain large in CMIP6 models but are reduced in HighResMIP, with OMIP models giving the best performance. The analysis suggests that, once local forcing errors have been reduced, the major source of the SETA biases lies in the equatorial Atlantic. This study shows that finer model resolution has helped reduce the local origin of the SETA SST bias but further developments of model physics schemes will be required to make progress.

AI models in health care are not colour blind and we should not be either

In the article by Judy Wawira Gichoya and colleagues,1 the authors found that artificial intelligence (AI) systems can be trained to determine a person's self-reported race based on a medical image, such as a chest or spinal x-ray image. The issue, which has been highlighted by others,2 is that if a model can easily determine a patient's race, it can use this information to predict other clinical outcomes. This is a scary prospect given the persistent bias against racial and ethnic minority groups in health care, leading to unequal access to care and unequal health, which are all also encoded in medical datasets.

Evaluation of High-Resolution Precipitation Products over the Rwenzori Mountains (Uganda)

Abstract The Rwenzori Mountains, in southwest Uganda, are prone to precipitation-related hazards such as flash floods and landslides. These natural hazards highly impact the lives and livelihoods of the people living in the region. However, our understanding of the precipitation patterns and their impact on related hazardous events and/or agricultural productivity is hampered by a dearth of in situ precipitation observations. Here, we propose an evaluation of gridded precipitation products as potential candidates filling this hiatus. We evaluate three state-of-the-art gridded products, the ERA5 reanalysis, IMERG satellite observations, and a simulation from the convection-permitting climate model (CPM), COSMO-CLM, for their ability to represent precipitation totals, timing, and precipitation probability density function. The evaluation is performed against observations from 11 gauge stations that provide at least 2.5 years of hourly and half-hourly data, recorded between 2011 and 2016. Results indicate a poor performance of ERA5 with a persistent wet bias, mostly for stations in the rain shadow of the mountains. IMERG gives the best representation of the precipitation totals as indicated by bias score comparisons. The CPM outperforms both ERA5 and IMERG in representing the probability density function, while both IMERG and the CPM have a good skill in capturing precipitation seasonal and diurnal cycles. The better performance of CPM is attributable to its higher resolution. This study highlights the potential of using IMERG and CPM precipitation estimates for hydrological and impact modeling over the Rwenzori Mountains, preferring IMERG for precipitation totals and CPM for precipitation extremes.

Consumers’ perceived and expected inflation in South Korea

ABSTRACT Household inflation expectations act as an important input in a central bank’s inflation-targeting monetary policy formulation. This study investigates the formation of households’ inflation expectations in South Korea based on a consumer survey conducted by the Bank of Korea. We use epidemiological models and find that households’ inflation expectations display strong and persistent overestimation bias. Furthermore, Bank of Korea’s inflation forecasts influence households’ inflation expectations. The findings suggest that the strong upward bias in households’ inflation expectations is consistent with the perceived inflation. The Bank of Korea needs to enhance its communication strategy to guide households’ inflation expectations based on its inflation targeting.

Resolution-Sensitive Added Value Analysis of CORDEX-CORE RegCM4-7 Past Seasonal Precipitation Simulations over Africa Using Satellite-Based Observational Products

This study adopts a two-way approach to CORDEX-CORE RegCM4-7 seasonal precipitation simulations’ Added Value (AV) analysis over Africa, which aims to quantify potential improvements introduced by the downscaling approach at high and low resolution, using satellite-based observational products. The results show that RegCM4-7 does add value to its driving Global Climate Models (GCMs) with a positive Added Value Coverage (AVC) ranging between 20 and 60% at high resolution, depending on the season and the boundary conditions. At low resolution, the results indicate an increase in the positive AVC by up to 20% compared to the high-resolution results, with an up to 8% decrease for instances where an increase is not observed. Typical climate zones such as West Africa, Central Africa, and Southern East Africa, where improvements by Regional Climate Models (RCMs) are expected due to strong dependence on mesoscale and fine-scale features, show positive AVC greater than 20%, regardless of the season and the driving GCM. These findings provide more evidence for confirming the hypothesis that the RCMs AV is influenced by their internal physics rather than being the product of a mere disaggregation of large-scale features provided by GCMs. Although the results show some dependencies to the driving GCMs relating to their equilibrium climate sensitivity nature, the findings at low resolutions similar to the native GCM resolutions make the influence of internal physics more important. The findings also feature the CORDEX-CORE RegCM4-7 precipitation simulations’ potential in bridging the quality and resolution gap between coarse GCMs and high-resolution remote sensing datasets. Even if further post-processing activities, such as bias correction, may still be needed to remove persistent biases at high resolution, using upscaled RCMs as an alternative to GCMs for large-scale precipitation studies over Africa can be insightful if the AV and other performance statistics are satisfactory for the intended application.

Threat-biased attention in childhood anxiety: A cognitive-affective developmental model

• Threat-biased attention is inconsistent in clinical and developmental literatures. • Threat-biased attention in childhood anxiety is a matter of relative magnitude. • Executive function and emotion regulation may relate to persistent threat biases. • Unique risk profiles are proposed for the development of anxiety in children. Threat-biased attention presents a paradox among clinical and developmental literatures: while biased attention toward threat has been implicated in childhood anxiety vulnerability, a parallel developmental literature highlights the early emergence of this bias as a normative pattern of attention associated with positive socioemotional outcomes. To bridge between these two literatures, evidence is reviewed across diverse domains (threat-biased attention, temperamental vulnerability, executive function, emotion regulation). An integrative developmental framework is proposed to elucidate how threat-biased attention may progress from a normative response to a more robust and persistent attentional bias associated with childhood anxiety. Deficits in developing executive function (inhibition and attentional control) and emotion regulation are proposed as reciprocal pathways to the perpetuation of this bias and the development of anxiety. Temperamental vulnerability is also considered as a risk factor. This model is most relevant for vigilant models of threat bias and may not account for threat-biases with diverse presentations (e.g., attention bias away from threat). Further, the current model is derived from studies highly reliant on behavioural reaction time and would benefit from validation across more diverse methods and paradigms for studying attentional bias. This model may help identify unique risk profiles for anxiety in children (e.g., deficits in either domain of executive function or emotion regulation with the other domain relatively intact). With further validation, these profiles may have clinical utility in informing the development of treatments that are tailored to address specific affected pathways – allowing for greater specificity in intervention selection.

Shaping sustainable harvest boundaries for marine populations despite estimation bias

AbstractBiased estimates of population status are a pervasive conservation problem. This problem has plagued assessments of commercial exploitation of marine species and can threaten the sustainability of both populations and fisheries. We develop a computer‐intensive approach to minimize adverse effects of persistent estimation bias in assessments by optimizing operational harvest measures (harvest control rules) with closed‐loop simulation of resource‐management feedback systems: management strategy evaluation. Using saithe (Pollachius virens), a bottom water, apex predator in the North Sea, as a real‐world case study, we illustrate the approach by first diagnosing robustness of the existing harvest control rule and then optimizing it through propagation of biases (overestimated stock abundance and underestimated fishing pressure) along with select process and observation uncertainties. Analyses showed that severe biases lead to overly optimistic catch limits and then progressively magnify the amplitude of catch fluctuation, thereby posing unacceptably high overharvest risks. Consistent performance of management strategies to conserve the resource can be achieved by developing more robust control rules. These rules explicitly account for estimation bias through a computational grid search for a set of control parameters (threshold abundance that triggers management action, Btrigger, and target exploitation rate, Ftarget) that maximize yield while keeping stock abundance above a precautionary level. When the biases become too severe, optimized control parameters—for saithe, raising Btrigger and lowering Ftarget—would safeguard against a overharvest risk (<3.5% probability of stock depletion) and provide short‐term stability in catch limit (<20% year‐to‐year variation), thereby minimizing disruption to fishing communities. The precautionary approach to fine‐tuning adaptive risk management through management strategy evaluation offers a powerful tool to better shape sustainable harvest boundaries for exploited resource populations when estimation bias persists. By explicitly accounting for emergent sources of uncertainty, our proposed approach ensures effective conservation and sustainable exploitation of living marine resources even under profound uncertainty.

Making Better (Investment) Decisions by Robert C. Jones (2016): A Journal Review

This article presents a review on the Journal of Robert C. Jones “Making Better Investment Decisions”. The vast literature and academic debates in finance presents a wide array of conceptual and theoretical frameworks on investing and decision making. Robert C. Jones, a Certified Financial Analyst, is an advocate of active investing and is affiliated with Systems Two Advisors. His works revolve around practical applications of making better (investment) decisions, active management in investments and machine learning for stock selection to name a few. His background is reflected in this article under critique, “Making Better (Investment) Decisions” which was published in 2016. In this article, Jones centered on how people make actual investment decisions, and presented advice on how to make better decisions. The main idea of his article states that a decision-making advantage is more feasible than an information advantage. This article is coherently organized, such that the first half presented a review of literature from cognitive psychology about how people make decisions, what are the persistent biases in human judgment and why they arise particularly emphasizing on the implications for investors. In the second part, this article discussed several established practices to improve decision making, especially centered on how investors may possibly use them.

An unbiased test reveals no enrichment of sexually antagonistic polymorphisms on the human X chromosome.

Mutations with beneficial effects in one sex can have deleterious effects in the other. Such ‘sexually antagonistic’ (SA) variants contribute to variation in life-history traits and overall fitness, yet their genomic distribution is poorly resolved. Theory predicts that SA variants could be enriched on the X chromosome or autosomes, yet current empirical tests face two formidable challenges: (i) identifying SA selection in genomic data is difficult; and (ii) metrics of SA variation show persistent biases towards the X, even when SA variants are randomly distributed across the genome. Here, we present an unbiased test of the theory that SA variants are enriched on the X. We first develop models for reproductive FST—a metric for quantifying sex-differential (including SA) effects of genetic variants on lifetime reproductive success—that control for X-linked biases. Comparing data from approximately 250 000 UK Biobank individuals to our models, we find FST elevations consistent with both X-linked and autosomal SA polymorphisms affecting reproductive success in humans. However, the extent of FST elevations does not differ from a model in which SA polymorphisms are randomly distributed across the genome. We argue that the polygenic nature of SA variation, along with sex asymmetries in SA effects, might render X-linked enrichment of SA polymorphisms unlikely.

Intercomparison of atmospheric forcing datasets and twoPBLschemes for precipitation modelling over a coastal valley in northern British Columbia, Canada

AbstractEnvironmental modelling of remote areas requires dynamical downscaling of meteorological data to obtain precipitation values that could substitute for sparse in‐situ observations. This study examined numerical simulations of precipitation over the Terrace‐Kitimat Valley, an industrializing corridor in the Coast Mountains of northern British Columbia, Canada. Modelling uncertainty was explored for 1 year of output from the Weather Research and Forecasting model at 1‐km grid spacing for three atmospheric forcing datasets and two planetary boundary layer (PBL) schemes. The observed total precipitation ranged from 1170 to 2380 mm and was often underestimated by more than 40% when using the North American Regional Reanalysis as atmospheric forcing data or the Mellor‐Yamada‐Nakanishi‐Niino level 3 (MYNN3) parameterization as PBL scheme. Persistent low bias from model configurations using these configurations suggested that merely selecting an alternative atmospheric forcing dataset does not ameliorate systematic error occasioned by a poorly performing PBL parameterization. Hence, the choice of the PBL scheme and the meteorological dataset is important for spatial estimation of precipitation using WRF. Model output best corresponded with annual gauge measurements when simulations with the Mellor‐Yamada‐Janjić (MYJ) PBL scheme were forced with ERA5. The North American Mesoscale Analyses (NAM‐ANL) however demonstrated better performance for monthly variation and high‐intensity precipitation than ERA5. Using both datasets therefore may be valuable for calculations related to environmental change. With either NAM‐ANL or ERA5 as atmospheric forcing data and MYJ as the PBL scheme, the uncertainty in annual simulated precipitation amount ranged between 38% overestimation and 21% underestimation of observational data.

Variational-Based Nonlinear Bayesian Filtering With Biased Observations

State estimation of dynamical systems is crucial for providing new decision-making and system automation information in different applications. However, the assumptions on the standard computational models for sensor measurements can be violated in practice due to different types of data abnormalities such as outliers and biases. In this work, we focus on the occurrence of measurement biases and propose a robust filter for their detection and mitigation during state estimation of nonlinear dynamical systems. We model the presence of bias in each dimension within the generative structure of the state-space models. Subsequently, employing the theory of Variational Bayes and general Gaussian filtering, we devise a recursive filter which we call the Bias Detecting and Mitigating (BDM) filter. As the error detection mechanism is embedded within the filter structure its dependence on any external detector is obviated. Simulations verify the performance gains of the proposed BDM filter compared to similar Kalman filtering-based approaches in terms of robustness to temporary and persistent bias presence.

Racial biases in healthcare: Examining the contributions of Point of Care tools and unintended practitioner bias to patient treatment and diagnosis.

Sophisticated algorithms are used daily to search through hundreds of medical journals in order to package updated medical insights into commercial databases. Healthcare practitioners can access these searchable databases-called Point of Care (PoC) tools-as downloadable apps on their smartphones or tablets to comprehensively and efficiently inform patient diagnosis and treatment. Because racist biases are unintentionally incorporated into the search reports that the companies generate and that practitioners regularly access, the aim of this article is to examine how healthcare practitioners' "pre-existing" racial stereotypes interact with pithy conclusions about race and ethnicity in PoC tools. I use qualitative research methods (content analysis, discourse analysis, open-ended semi-structured interviews, and role play) to frame the analysis within the Public Health Critical Race Praxis (PHCRP). This approach facilitates an understanding of how biological racism-the use of scientific evidence to support inherent differences between races-that is embedded in PoC algorithms informs a practitioner's assessment of a patient, and converges with persistent racial bias in medical training, medical research and healthcare. I contextualize the study with one semi-structured interview with an Editor of a leading PoC tool, MedScope (pseudonomized), and 10 semi-structured interviews with healthcare practitioners in S.E. Ontario, Canada. The article concludes that PoC tools and practitioners' personal biases contribute to racial prejudices in healthcare provision. This warrants further research on racial bias in medical literature and curriculum design in medical school.

Film, Music, Memory, by Berthold Hoeckner

Review| December 01 2021 Film, Music, Memory, by Berthold Hoeckner Film, Music, Memory, by BertholdHoeckner. Cinema and Modernity. Chicago: University of Chicago Press, 2019. viii, 278 pp. Julie Hubbert Julie Hubbert JULIE HUBBERT is the LaDare Robinson Memorial Professor of Music at the University of South Carolina where she holds appointments in both the School of Music and Film and Media Studies. Her current book project, supported by a National Endowment for the Humanities Fellowship, is entitled Technology, Labor, and Listening: Music in New Hollywood Film (1968–1980), and is under contract with Oxford University Press. Search for other works by this author on: This Site PubMed Google Scholar Journal of the American Musicological Society (2021) 74 (3): 681–687. https://doi.org/10.1525/jams.2021.74.3.681 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter LinkedIn Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation Julie Hubbert; Film, Music, Memory, by Berthold Hoeckner. Journal of the American Musicological Society 1 December 2021; 74 (3): 681–687. doi: https://doi.org/10.1525/jams.2021.74.3.681 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search nav search search input Search input auto suggest search filter All ContentJournal of the American Musicological Society Search The idea of a “mind’s eye,” of externalizing internal thoughts and memories, has been a preoccupation of writers throughout literature. From Chaucer and Shakespeare to Philip K. Dick and Greg Daniels, who recently penned the 2019 hit Amazon series Upload, writers have long been interested in examining how and why the brain remembers. With the introduction of mechanical remembering devices—photographs, sound recordings, film, television, and streaming media—a parallel “culture of memory” has blossomed. Berthold Hoeckner’s book Film, Music, Memory considers the privileged place cinema has had in this culture. Because of its ability to see and hear, unconsciously record and be consciously edited, film has changed not only the way we dream, but also how we remember and visualize the working of the mind’s eye. Like most film music scholarship, Hoeckner’s study is fundamentally a restoration project. It works to counter film scholarship’s persistent visual bias by reminding critics... You do not currently have access to this content.

Mothers’ Post-separation Experiences of Male Partner Abuse: An Exploratory Study

Intimate Partner Violence (IPV) involves physical and sexual violence and coercive controlling behaviours to maximise power inequality in abusive relationships. Many women make the decision to exit abusive relationships due to the detrimental impact of IPV on their children. In a qualitative exploration, semi-structured in-depth interviews were conducted with 10 women recruited by purposive and snowballing sampling techniques. Two overarching themes of single mothers’ unique experiences of co-parenting with IPV were revealed through interpretative phenomenological analysis. The first theme was Continuous Victimisation, which indicated that post-separation victimisation was an extension of existing IPV whereby fathers used intimidation, threatening behaviours such as stalking and other monitoring tactics and the deliberate undermining of the mother–child relationship. The second theme identified was Systemic Challenges, indicating how court officials applied a ‘pro-contact’ approach and either minimised or denied mothers’ allegations of IPV and the impact on them and the children. The analysis found a persistent bias against mothers . Implications of the study are discussed before the article concludes that attitudinal change regarding IPV is required by decision makers in court processes with a recognition that abusive men may be unwilling to engage in cooperative parenting that focuses on the children’s developmental, social, emotional, psychological and physical needs.

Evaluating Global Climate Models for Hydrological Studies of the Upper Colorado River Basin

AbstractThree generations of global climate models (GCMs), Coupled Model Intercomparison Project version 3 (CMIP3), CMIP5, and CMIP6, are evaluated for performance simulating seasonal mean and annual‐to‐decadal variability of temperature and precipitation in the Upper Colorado River Basin. Low‐frequency precipitation variability associated with drought is a particular focus and found to be a significant model shortcoming. The evaluation includes remote teleconnected atmospheric responses to the Pacific Ocean, including the El Niño/Southern Oscillation and Pacific Decadal Oscillation. GCMs have improved their simulation of the Upper Basin over model generations, but primarily in atmospheric circulation metrics. Persistent winter precipitation biases have changed little, including in multiyear precipitation variability. Users generally bias‐corrected GCM data before use; evaluation using a simple spatially and temporally averaged bias correction shows that the CMIP6 models outperform earlier generations after the bias correction, although more complex precipitation biases remain even after the simple bias correction. These model rankings will be useful when selecting GCMs for a variety of hydrological and ecological climate studies in the Upper Basin.

Subpolar Southern Ocean Response to Changes in the Surface Momentum, Heat, and Freshwater Fluxes under 2xCO2

AbstractThe Antarctic subpolar Southern Ocean (sSO) has fundamental climate importance. Antarctic Bottom Water (AABW) originates in the sSO and supplies the lower limb of the meridional overturning circulation (MOC), occupying 36% of ocean volume. Climate models struggle to represent continental shelf processes that form AABW. We explore sources of persistent model biases by examining response of the sSO to perturbations in surface forcing in a global ocean–sea ice model (ACCESS-OM2) that forms AABW both on shelf and in open ocean. The sSO response to individual and combined perturbations of surface heat, freshwater, and momentum fluxes follows the WCRP CMIP6 FAFMIP-protocol. Wind perturbation (i.e., a poleward shift and intensification of the westerlies) is dominant, enhancing AABW formation and accelerating the global MOC. This occurs through upwelling of warm waters and inhibition of sea ice growth during winter, which triggers large open water polynya (OWP) events with associated deep convection. These events occur in the Weddell and Ross Seas and their variability is associated with availability of heat at midocean depths. These OWPs cease when the heat reservoir is depleted. Effects of surface warming and freshening only partially compensate changes from increasing winds on ocean stratification and depletion of AABW formation. These results indicate that overly convective models, such ACCESS-OM2, can respond to CO2-perturbed scenarios by forming too much AABW in OWP, which might not hold in models without OWPs. This might contribute to the large intermodel spread thermosteric sea level projections, being relevant to the interpretation of future projections by current climate models.

Identity Politics, Justice, and the Quest for Solidarity

Abstract This article situates arguments against identity politics within a broader context of philosophical and political arguments about identity and the subject. One pervasive argument is that identity politics is a key factor working against social solidarity in a pluralistic democracy. In order to contest this claim, I use the work of feminist theorists who address a persistent bias against collective identity in Western philosophical thought. Finally, I argue that the practice of social solidarity requires not jettisoning identity, but re-conceptualizing identity in terms of narrative, which can serve as a basis for understanding one’s moral responsibility to others.