Difference Blindness vs. Bias Awareness: Why Law Firms with the Best of Intentions Have Failed to Create Diverse Partnerships

This Article uses the example of BigLaw firms to explore the challenges that many elite organizations face in providing equal opportunity to their workers. Despite good intentions and the investment of significant resources, large law firms have been consistently unable to deliver diverse partnership structures - especially in more senior positions of power. Building on implicit and institutional bias scholarship and on successful approaches described in the organizational behavior literature, we argue that a significant barrier to systemic diversity at the law firm partnership level has been, paradoxically, the insistence on difference blindness standards that seek to evaluate each person on their individual merit. While powerful in dismantling intentional discrimination, these standards rely on an assumption that lawyers are, and have the power to act as, atomistic individuals - a dangerous assumption that has been disproven consistently by the literature establishing the continuing and powerful influence of implicit and institutional bias. Accordingly, difference blindness, which holds all lawyers accountable to seemingly neutral standards, disproportionately disadvantages diverse populations and normalizes the dominance of certain actors - here, white men - by creating the illusion that success or failure depends upon individual rather than structural constraints. In contrast, we argue that a bias awareness approach that encourages identity awareness and a relational framework is a more promising way to promote equality, equity, and inclusion.

Alternative harvest strategies to alleviate predation bottlenecks: a bioeconomic modeling approach to inform adaptive management

Thesis (Ph.D.)--University of Washington, 2016-06

Synovial sarcoma - Towards a simplified approach to prognosis

Background: Synovial sarcoma is a high grade sarcoma that usually occurs in adults. Numerous studies have attempted to identify prognostic factors that might allow more effective treatment for particular subgroups of patients. Methods: We studied 25 histologically confirmed cases of synovial sarcoma in an attempt to identify particular patient, tumour or treatment characteristics that might have a prognostic significance using Cox proportional hazards regression modelling to identify differences in survival rates. All patients received their definitive surgical treatment from a single orthopaedic surgeon reducing the likelihood of bias related to variations in surgical technique. Results: Statistically significant higher survival rates were seen in female patients (P = 0.040) and in patients aged

Estimation of TFP growth:a semiparametric smooth coefficient approach

This article uses a semiparametric smooth coefficient model (SPSCM) to estimate TFP growth and its components (scale and technical change). The SPSCM is derived from a nonparametric specification of the production technology represented by an input distance function (IDF), using a growth formulation. The functional coefficients of the SPSCM come naturally from the model and are fully flexible in the sense that no functional form of the underlying production technology is used to derive them. Another advantage of the SPSCM is that it can estimate bias (input and scale) in technical change in a fully flexible manner. We also used a translog IDF framework to estimate TFP growth components. A panel of U.S. electricity generating plants for the period 1986–1998 is used for this purpose. Comparing estimated TFP growth results from both parametric and semiparametric models against the Divisia TFP growth, we conclude that the SPSCM performs the best in tracking the temporal behavior of TFP growth.

Investigating G protein signalling bias at the glucagon-like peptide-1 receptor in yeast

Background and Purpose The glucagon-like peptide 1 (GLP-1) receptor performs an important role in glycaemic control, stimulating the release of insulin. It is an attractive target for treating type 2 diabetes. Recently, several reports of adverse side effects following prolonged use of GLP-1 receptor therapies have emerged: most likely due to an incomplete understanding of signalling complexities. Experimental Approach We describe the expression of the GLP-1 receptor in a panel of modified yeast strains that couple receptor activation to cell growth via single Gα/yeast chimeras. This assay enables the study of individual ligand-receptor G protein coupling preferences and the quantification of the effect of GLP-1 receptor ligands on G protein selectivity. Key Results The GLP-1 receptor functionally coupled to the chimeras representing the human Gαs, Gαi and Gαq subunits. Calculation of the dissociation constant for a receptor antagonist, exendin-3 revealed no significant difference between the two systems. We obtained previously unobserved differences in G protein signalling bias for clinically relevant therapeutic agents, liraglutide and exenatide; the latter displaying significant bias for the Gαi pathway. We extended the use of the system to investigate small-molecule allosteric compounds and the closely related glucagon receptor. Conclusions and Implications These results provide a better understanding of the molecular events involved in GLP-1 receptor pleiotropic signalling and establish the yeast platform as a robust tool to screen for more selective, efficacious compounds acting at this important class of receptors in the future. © 2014 The Authors. British Journal of Pharmacology published by John Wiley & Sons Ltd on behalf of The British Pharmacological Society.

Making hiring decisions:a grounded theory approach to a practise-based study of filtering in selection

This thesis objective is to discover “How are informal decisions reached by screeners when filtering out undesirable job applications?” Grounded theory techniques were employed in the field to observe and analyse informal decisions at the source by screeners in three distinct empirical studies. Whilst grounded theory provided the method for case and cross-case analysis, literature from academic and non-academic sources was evaluated and integrated to strengthen this research and create a foundation for understanding informal decisions. As informal decisions in early hiring processes have been under researched, this thesis contributes to current knowledge in several ways. First, it locates the Cycle of Employment which enhances Robertson and Smith’s (1993) Selection Paradigm through the integration of stages that individuals occupy whilst seeking employment. Secondly, a general depiction of the Workflow of General Hiring Processes provides a template for practitioners to map and further develop their organisational processes. Finally, it highlights the emergence of the Locality Effect, which is a geographically driven heuristic and bias that can significantly impact recruitment and informal decisions. Although screeners make informal decisions using multiple variables, informal decisions are made in stages as evidence in the Cycle of Employment. Moreover, informal decisions can be erroneous as a result of a majority and minority influence, the weighting of information, the injection of inappropriate information and criteria, and the influence of an assessor. This thesis considers these faults and develops a basic framework of understanding informal decisions to which future research can be launched.

Quantifying Selection Bias from Birth History Estimates of Child Mortality

Thesis (Master's)--University of Washington, 2016-08

Der Anti-Bias-Ansatz als Methode politischer Erwachsenenbildung

Vorurteile, Macht und Diskriminierung sind die zentralen Themen des Anti-Bias-Ansatzes, der Diskriminierung auf zwischenmenschlicher, struktureller und gesellschaftlich-kultureller Ebene berücksichtigt. Die eigene Verwobenheit in Machtverhältnisse und damit verbundene Erfahrungen von Diskriminierung und Privilegierung sind dabei der Ausgangspunkt des Lernens. Vision ist eine vorurteilsbewusste, diskriminierungskritische und machtsensible Gesellschaft. Der vorliegende Beitrag stellt den Anti-Bias-Ansatz als Methode politischer Erwachsenenbildung vor. Hierfür werden die (Macht-)Mechanismen der Entstehung, Verinnerlichung und Reproduktion von Vorurteilen und Diskriminierung beleuchtet und wird die Intersektionalität (Überkreuzung) verschiedener Differenzlinien ausgemacht und aufgedeckt. Den Abschluss bildet ein Beispiel konkreter Umsetzung. (DIPF/Orig.)

SAMPLING BIAS IN EVALUATING THE PROBABILITY OF SEISMICALLY INDUCED SOIL LIQUEFACTION WITH SPT & CPT CASE HISTORIES

Several deterministic and probabilistic methods are used to evaluate the probability of seismically induced liquefaction of a soil. The probabilistic models usually possess some uncertainty in that model and uncertainties in the parameters used to develop that model. These model uncertainties vary from one statistical model to another. Most of the model uncertainties are epistemic, and can be addressed through appropriate knowledge of the statistical model. One such epistemic model uncertainty in evaluating liquefaction potential using a probabilistic model such as logistic regression is sampling bias. Sampling bias is the difference between the class distribution in the sample used for developing the statistical model and the true population distribution of liquefaction and non-liquefaction instances. Recent studies have shown that sampling bias can significantly affect the predicted probability using a statistical model. To address this epistemic uncertainty, a new approach was developed for evaluating the probability of seismically-induced soil liquefaction, in which a logistic regression model in combination with Hosmer-Lemeshow statistic was used. This approach was used to estimate the population (true) distribution of liquefaction to non-liquefaction instances of standard penetration test (SPT) and cone penetration test (CPT) based most updated case histories. Apart from this, other model uncertainties such as distribution of explanatory variables and significance of explanatory variables were also addressed using KS test and Wald statistic respectively. Moreover, based on estimated population distribution, logistic regression equations were proposed to calculate the probability of liquefaction for both SPT and CPT based case history. Additionally, the proposed probability curves were compared with existing probability curves based on SPT and CPT case histories.

BIAS-CORRECTED MAXIMUM LIKELIHOOD ESTIMATION OF THE PARAMETERS OF THE WEIGHTED LINDLEY DISTRIBUTION

This report discusses the calculation of analytic second-order bias techniques for the maximum likelihood estimates (for short, MLEs) of the unknown parameters of the distribution in quality and reliability analysis. It is well-known that the MLEs are widely used to estimate the unknown parameters of the probability distributions due to their various desirable properties; for example, the MLEs are asymptotically unbiased, consistent, and asymptotically normal. However, many of these properties depend on an extremely large sample sizes. Those properties, such as unbiasedness, may not be valid for small or even moderate sample sizes, which are more practical in real data applications. Therefore, some bias-corrected techniques for the MLEs are desired in practice, especially when the sample size is small. Two commonly used popular techniques to reduce the bias of the MLEs, are ‘preventive’ and ‘corrective’ approaches. They both can reduce the bias of the MLEs to order O(n−2), whereas the ‘preventive’ approach does not have an explicit closed form expression. Consequently, we mainly focus on the ‘corrective’ approach in this report. To illustrate the importance of the bias-correction in practice, we apply the bias-corrected method to two popular lifetime distributions: the inverse Lindley distribution and the weighted Lindley distribution. Numerical studies based on the two distributions show that the considered bias-corrected technique is highly recommended over other commonly used estimators without bias-correction. Therefore, special attention should be paid when we estimate the unknown parameters of the probability distributions under the scenario in which the sample size is small or moderate.

Detectability of migrating raptors and its effect on bias and precision of trend estimates

Annual counts of migrating raptors at fixed observation points are a widespread practice, and changes in numbers counted over time, adjusted for survey effort, are commonly used as indices of trends in population size. Unmodeled year-to-year variation in detectability may introduce bias, reduce precision of trend estimates, and reduce power to detect trends. We conducted dependent double-observer surveys at the annual fall raptor migration count at Lucky Peak, Idaho, in 2009 and 2010 and applied Huggins closed-capture removal models and information-theoretic model selection to determine the relative importance of factors affecting detectability. The most parsimonious model included effects of observer team identity, distance, species, and day of the season. We then simulated 30 years of counts with heterogeneous individual detectability, a population decline (λ = 0.964), and unexplained random variation in the number of available birds. Imperfect detectability did not bias trend estimation, and increased the time required to achieve 80% power by less than 11%. Results suggested that availability is a greater source of variance in annual counts than detectability; thus, efforts to account for availability would improve the monitoring value of migration counts. According to our models, long-term trends in observer efficiency or migratory flight distance may introduce substantial bias to trend estimates. Estimating detectability with a novel count protocol like our double-observer method is just one potential means of controlling such effects. The traditional approach of modeling the effects of covariates and adjusting the index may also be effective if ancillary data is collected consistently.

Customer Loyalty toward grocery retailing service: a multidimensional approach to costumer perceived value

Structured abstract Purpose: To deepen, in grocery retail context, the roles of consumer perceived value and consumer satisfaction, as antecedents’ dimensions of customer loyalty intentions. Design/Methodology/approach: Also employing a short version (12-items) of the original 19-item PERVAL scale of Sweeney & Soutar (2001), a structural equation modeling approach was applied to investigate statistical properties of the indirect influence on loyalty of a reflective second order customer perceived value model. The performance of three alternative estimation methods was compared through bootstrapping techniques. Findings: Results provided i) support for the use of the short form of the PERVAL scale in measuring consumer perceived value; ii) the influence of the four highly correlated independent latent predictors on satisfaction was well summarized by a higher-order reflective specification of consumer perceived value; iii) emotional and functional dimensions were determinants for the relationship with the retailer; iv) parameter’s bias with the three methods of estimation was only significant for bootstrap small sample sizes. Research limitations:/implications: Future research is needed to explore the use of the short form of the PERVAL scale in more homogeneous groups of consumers. Originality/value: Firstly, to indirectly explain customer loyalty mediated by customer satisfaction it was adopted a recent short form of PERVAL scale and a second order reflective conceptualization of value. Secondly, three alternative estimation methods were used and compared through bootstrapping and simulation procedures.

Clinical Accuracy of the MedGem Indirect Calorimeter for Measuring Resting Energy Expenditure in Cancer Patients

OBJECTIVE: To compare, in patients with cancer and in healthy subjects, measured resting energy expenditure (REE) from traditional indirect calorimetry to a new portable device (MedGem) and predicted REE. DESIGN: Cross-sectional clinical validation study. SETTING: Private radiation oncology centre, Brisbane, Australia. SUBJECTS: Cancer patients (n = 18) and healthy subjects (n = 17) aged 37-86 y, with body mass indices ranging from 18 to 42 kg/m(2). INTERVENTIONS: Oxygen consumption (VO(2)) and REE were measured by VMax229 (VM) and MedGem (MG) indirect calorimeters in random order after a 12-h fast and 30-min rest. REE was also calculated from the MG without adjustment for nitrogen excretion (MGN) and estimated from Harris-Benedict prediction equations. Data were analysed using the Bland and Altman approach, based on a clinically acceptable difference between methods of 5%. RESULTS: The mean bias (MGN-VM) was 10% and limits of agreement were -42 to 21% for cancer patients; mean bias -5% with limits of -45 to 35% for healthy subjects. Less than half of the cancer patients (n = 7, 46.7%) and only a third (n = 5, 33.3%) of healthy subjects had measured REE by MGN within clinically acceptable limits of VM. Predicted REE showed a mean bias (HB-VM) of -5% for cancer patients and 4% for healthy subjects, with limits of agreement of -30 to 20% and -27 to 34%, respectively. CONCLUSIONS: Limits of agreement for the MG and Harris Benedict equations compared to traditional indirect calorimetry were similar but wide, indicating poor clinical accuracy for determining the REE of individual cancer patients and healthy subjects.