The Gay Accent, Gender, and Title VII Employment Discrimination

While race, religion, ethnicity, and sex will always remain salient social issues in our nation, sexual orientation is currently at the forefront of our national debate and will likely not abate in the foreseeable future. Federal courts, for example, struggle in differentiating sex, gender, and sexuality when adjudicating Title VII employment discrimination claims. Because Title VII does not protect employees from sexual orientation-based discrimination, plaintiffs who are or are perceived to be of a sexual minority have difficulty proving a valid sex-based discrimination claim in federal court. This difficulty arises because one cannot perceive sex, gender, and sexuality without muddling the stereotypes associated with each one. Social science can help separate gender and sex characteristics from sexual characteristics; these distinctions expose deeper social biases toward sex, gender, and sexuality. This Comment examines one of these characteristics: the male voice. Federal courts have addressed alleged discrimination partly based on a male employee’s gay or effeminate voice in six cases, with mixed results. This Comment argues that when male employees are discriminated against partly based on their voice being perceived as gay—what I term the gay accent—this discrimination should be seen as sex discrimination through a mixed-motive analysis

The Gay Accent, Gender, and Title VII Employment Discrimination

While race, religion, ethnicity, and sex will always remain salient social issues in our nation, sexual orientation is currently at the forefront of our national debate and will likely not abate in the foreseeable future. Federal courts, for example, struggle in differentiating sex, gender, and sexuality when adjudicating Title VII employment discrimination claims. Because Title VII does not protect employees from sexual orientation-based discrimination, plaintiffs who are or are perceived to be of a sexual minority have difficulty proving a valid sex-based discrimination claim in federal court. This difficulty arises because one cannot perceive sex, gender, and sexuality without muddling the stereotypes associated with each one. Social science can help separate gender and sex characteristics from sexual characteristics; these distinctions expose deeper social biases toward sex, gender, and sexuality. This Comment examines one of these characteristics: the male voice. Federal courts have addressed alleged discrimination partly based on a male employee’s gay or effeminate voice in six cases, with mixed results. This Comment argues that when male employees are discriminated against partly based on their voice being perceived as gay—what I term the gay accent—this discrimination should be seen as sex discrimination through a mixed-motive analysis

Factors for Identifying Non-Anthropic Conscious Systems

One of the problems of identifying consciousness is defining it in ways that allow for universal application and exploration.  Popular and anthropocentric definitions are problematic due to their inherent bias toward exclusively biological events in a field of study that does not require and is even hindered by this limitation.  A preliminary definition is needed that would encompass known biological consciousness as well as theoretical macro, micro, and intrinsic levels of consciousness.  This paper proposes that the following are a preliminary set of factors for openly exploring what can be considered conscious with no biological or cultural biases. 1.        Communication: Consciousness requires discrete parts of the system to be able to influence one another in a holistic manner.  Whether this is by synaptic firing or gravitic relationships is irrelevant. 2.        Adaptation: Consciousness requires adaptation to its environment.  Note the avoidance of the popular term "awareness," which is an untestable factor on many levels.  Static systems cannot be conscious.  Dynamic systems can be, but are not necessarily conscious. 3.        Complexity: In order to be differentiated from purely physical or chemical dynamic systems, conscious systems must display a sufficient complexity in energy rate density.  This paper proposes a ɸm (erg/second/gram) of a minimum of 103 for any given system to be considered complex enough to display consciousness.  This is equivalent to the simplest lifeforms considered conscious. The first two requirements are easily understood.  The requirement of complexity is the least conventional and requires explication.  Physical complexity is often used as a basic threshold for organization, but this seems to be due to convenience more than logical applicability, especially when informational systems are weighed on their quantitative value.  It does not follow that a greater number of components translates to a higher threshold of complexity, any more than saying a bucket of sand is more physically complex than an iPad because it has more particulates. As Eric Chaisson posits, energy rate density is a more universal and reliable means of organizing complexity.  Energy rate density (ERD) measures the energy flow in ergs per gram per second within a given system.  This qualitative assessment of energy efficiency is more insightful than listing non-adaptive arrangements such as physical interactions or even systems theory.  The dramatic spike in ERD for all known conscious systems makes this an ideal metric for exploring radically different systems about which little else is known

Use of Discrete Choice Experiments in health economics: An update of the literature

The vast majority of stated preference research in health economics has been conducted in the random utility model paradigm using discrete choice experiments (DCEs). Ryan and Gerard (2003) have reviewed the applications of DCEs in the field of health economics. We have updated this initial work to include studies published between 2001 and 2007. Following the methods of Ryan and Gerard, we assess the later body of work, with respect to the key characteristics of DCEs such as selection of attributes and levels, experimental design, preference measurement, estimation procedure and validity. Comparisons between the periods are undertaken in order to identify any emerging trends.discrete choice experiments, health economics

A half-century diversion of monetary policy? An empirical horse-race to identify the UK variable most likely to deliver the desired nominal GDP growth rate

open access articleThe financial crisis of 2007–2008 triggered monetary policy designed to boost nominal demand, including ‘Quantitative Easing’, ‘Credit Easing’, ‘Forward Guidance’ and ‘Funding for Lending’. A key aim of these policies was to boost the quantity of bank credit to the non-financial corporate and household sectors. In the previous decades, however, policy-makers had not focused on bank credit. Indeed, over the past half century, different variables were raised to prominence in the quest to achieve desired nominal GDP outcomes. This paper conducts a long-overdue horse race between the various contenders in terms of their ability to account for observed nominal GDP growth, using a half-century of UK data since 1963. Employing the ‘General-to-Specific’ methodology, an equilibrium-correction model is estimated suggesting a long-run cointegrating relationship between disaggregated real economy credit and nominal GDP. Short-term and long-term interest rates and broad money do not appear to influence nominal GDP significantly. Vector autoregression and vector error correction modelling shows the real economy credit growth variable to be strongly exogenous to nominal GDP growth. Policy-makers are hence right to finally emphasise the role of bank credit, although they need to disaggregate it and specifically target bank credit for GDP-transactions

Cues and knowledge structures used by mental-health professionals when making risk assessments

Background: Research into mental-health risks has tended to focus on epidemiological approaches and to consider pieces of evidence in isolation. Less is known about the particular factors and their patterns of occurrence that influence clinicians’ risk judgements in practice. Aims: To identify the cues used by clinicians to make risk judgements and to explore how these combine within clinicians’ psychological representations of suicide, self-harm, self-neglect, and harm to others. Method: Content analysis was applied to semi-structured interviews conducted with 46 practitioners from various mental-health disciplines, using mind maps to represent the hierarchical relationships of data and concepts. Results: Strong consensus between experts meant their knowledge could be integrated into a single hierarchical structure for each risk. This revealed contrasting emphases between data and concepts underpinning risks, including: reflection and forethought for suicide; motivation for self-harm; situation and context for harm to others; and current presentation for self-neglect. Conclusions: Analysis of experts’ risk-assessment knowledge identified influential cues and their relationships to risks. It can inform development of valid risk-screening decision support systems that combine actuarial evidence with clinical expertise

An Evolutionary Approach to Class Disjointness Axiom Discovery

International audienceAxiom learning is an essential task in enhancing the quality of an ontology, a task that sometimes goes under the name of ontology enrichment. To overcome some limitations of recent work and to contribute to the growing library of ontology learning algorithms, we propose an evolutionary approach to automatically discover axioms from the abundant RDF data resource of the Semantic Web. We describe a method applying an instance of an Evolutionary Algorithm, namely Grammatical Evolution, to the acquisition of OWL class dis-jointness axioms, one important type of OWL axioms which makes it possible to detect logical inconsistencies and infer implicit information from a knowledge base. The proposed method uses an axiom scoring function based on possibility theory and is evaluated against a Gold Standard, manually constructed by knowledge engineers. Experimental results show that the given method possesses high accuracy and good coverage

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts