Veterinary medicine has undergone dramatic, rapid feminization while in many ways remaining gendered masculine. With women constituting approximately half of its practitioners and nearly 80 percent of students, veterinary medicine is the most feminized of the comparable health professions. Nevertheless, the culture of veterinary medicine glorifies stereotypically masculine actions and attitudes. This article examines how women veterinarians understand the gender dynamics within the profession. Our analysis reveals that the discursive strategies available to women sustain and justify the status quo, and (...) thus preserve hegemonic masculinity. Women use strategies previously used toward female tokens in nontraditional jobs, such as role encapsulation, and strategies previously used by male tokens in traditionally female jobs, such as distancing from the feminine. Through this discursive “gender work,” women help to maintain the institutionalized inequality and the masculine ethic of the profession. Veterinary medicine illustrates the importance of considering organizational context in studies of feminization. (shrink)

Within veterinary medical education, tracking systems exist that differentiate between “large” and “small” animal medicine. In a tracking system, students can focus primarily on their choice of animal medicine once they have completed the core curriculum. This article argues that these socially created categories are ever shifting; therefore, some species do not always “fit.” This generates new discourses surrounding emerging “border tracks”; these “tracks” focus on species whose social definitions change so that their placement in the tracking system of veterinary (...) medical education is a site of contestation. Thus, animal medicine operates not solely on the basis of biology, but on the basis of social meaning as well. This analysis will use the equine concentration to demonstrate the ambiguity of borders, as well as their potential to serve as communicative sites for social change. (shrink)

Infants have been described as 'statistical learners' capable of extracting structure (such as words) from patterned input (such as language). Here, we investigated whether prior knowledge influences how infants track transitional probabilities in word segmentation tasks. Are infants biased by prior experience when engaging in sequential statistical learning? In a laboratory simulation of learning across time, we exposed 9- and 10-month-old infants to a list of either disyllabic or trisyllabic nonsense words, followed by a pause-free speech stream composed of a (...) different set of disyllabic or trisyllabic nonsense words. Listening times revealed successful segmentation of words from fluent speech only when words were uniformly disyllabic or trisyllabic throughout both phases of the experiment. Hearing trisyllabic words during the pre-exposure phase derailed infants' abilities to segment speech into disyllabic words, and vice versa. We conclude that prior knowledge about word length equips infants with perceptual expectations that facilitate efficient processing of subsequent language input. (shrink)

Natural languages contain many layers of sequential structure, from the distribution of phonemes within words to the distribution of phrases within utterances. However, most research modeling language acquisition using artificial languages has focused on only one type of distributional structure at a time. In two experiments, we investigated adult learning of an artificial language that contains dependencies between both adjacent and non-adjacent words. We found that learners rapidly acquired both types of regularities and that the strength of the adjacent statistics (...) influenced learning of both adjacent and non-adjacent dependencies. Additionally, though accuracy was similar for both types of structure, participants’ knowledge of the deterministic non-adjacent dependencies was more explicit than their knowledge of the probabilistic adjacent dependencies. The results are discussed in the context of current theories of statistical learning and language acquisition. (shrink)

How do listeners learn about the statistical regularities underlying musical harmony? In traditional Western music, certain chords predict the occurrence of other chords: Given a particular chord, not all chords are equally likely to follow. In Experiments 1 and 2, we investigated whether adults make use of statistical information when learning new musical structures. Listeners were exposed to a novel musical system containing phrases generated using an artificial grammar. This new system contained statistical structure quite different from Western tonal music. (...) Our results suggest that learners take advantage of the statistical patterning of chords to acquire new musical structures, similar to learning processes previously observed for language learning. (shrink)

When a toddler knows a word, what does she actually know? Many categories have multiple relevant properties; for example, shape and color are relevant to membership in the category banana. How do toddlers prioritize these properties when recognizing familiar words, and are there systematic differences among children? In this study, toddlers viewed pairs of objects associated with prototypical colors. On some trials, objects were typically colored ; on other trials, colors were switched. On each trial, toddlers were directed to find (...) a target object. Overall, recognition was disrupted when colors were switched, as measured by eye movements. Moreover, individual differences in vocabularies predicted recognition differences: Toddlers who say fewer shape-based words were more disrupted by color switches. “Knowing” a word may not mean the same thing for all toddlers; different toddlers prioritize different facets of familiar objects in their lexical representations. (shrink)

In this article, we consider infants’ acquisition of foundational aspects of language and emotion through the lens of statistical learning. By taking a comparative developmental approach, we highlight ways in which the learning problems presented by input from these two rich communicative domains are both similar and different. Our goal is to encourage other scholars to consider multiple domains of human experience when developing theories in developmental cognitive science.

Recent research has begun to explore individual differences in statistical learning, and how those differences may be related to other cognitive abilities, particularly their effects on language learning. In this research, we explored a different type of relationship between language learning and statistical learning: the possibility that learning a new language may also influence statistical learning by changing the regularities to which learners are sensitive. We tested two groups of participants, Mandarin Learners and Naïve Controls, at two time points, 6 (...) months apart. At each time point, participants performed two different statistical learning tasks: an artificial tonal language statistical learning task and a visual statistical learning task. Only the Mandarin-learning group showed significant improvement on the linguistic task, whereas both groups improved equally on the visual task. These results support the view that there are multiple influences on statistical learning. Domain-relevant experiences may affect the regularities that learners can discover when presented with novel stimuli. (shrink)

We agree with Berger and Miller that the focus on cultural competence in medical education fails to name or confront key drivers of health inequity such as structural racism, social determin...

Childress et al. (2023) critically examine claims used to support unilateral withdrawal of life-sustaining ECMO over the objections of capacitated patients. The authors raise important concerns abo...

The first Young Researchers in Human–Robot Interaction Workshop, held on March 1, 2006 in Salt Lake City, Utah, provides insight into how to facilitate the establishment of the HRI community. Organized in conjunction with the first annual ACM/IEEE Human Robot Interaction Conference, the NSF-sponsored workshop assembled 15 graduate students from 5 different countries in computer science, psychology, engineering, and the arts to build the HRI community. This report highlights recommendations from discussion sessions, a synopsis of the plenary address, and representative (...) examples of the participants’ presentations. Participants emphasized that HRI is a unique field, requiring knowledge in computing, psychology, and communications despite the differences in the courses, methods, and philosophies across disciplines. The following are needed for future growth in HRI: stable, canonical robotics platforms for research purposes, a multidisciplinary community infrastructure to connect researchers, and a “Berlitz phrasebook” and collected reference materials for helping understand the “other” disciplines. (shrink)

The first Young Researchers in Human–Robot Interaction Workshop, held on March 1, 2006 in Salt Lake City, Utah, provides insight into how to facilitate the establishment of the HRI community. Organized in conjunction with the first annual ACM/IEEE Human Robot Interaction Conference, the NSF-sponsored workshop assembled 15 graduate students from 5 different countries in computer science, psychology, engineering, and the arts to build the HRI community. This report highlights recommendations from discussion sessions, a synopsis of the plenary address, and representative (...) examples of the participants’ presentations. Participants emphasized that HRI is a unique field, requiring knowledge in computing, psychology, and communications despite the differences in the courses, methods, and philosophies across disciplines. The following are needed for future growth in HRI: stable, canonical robotics platforms for research purposes, a multidisciplinary community infrastructure to connect researchers, and a “Berlitz phrasebook” and collected reference materials for helping understand the “other” disciplines. (shrink)

Machine learning algorithms may radically improve our ability to diagnose and treat disease. For moral, legal, and scientific reasons, it is essential that doctors and patients be able to understand and explain the predictions of these models. Scalable, customisable, and ethical solutions can be achieved by working together with relevant stakeholders, including patients, data scientists, and policy makers.

Peer review is a widely accepted instrument for raising the quality of science. Peer review limits the enormous unstructured influx of information and the sheer amount of dubious data, which in its absence would plunge science into chaos. In particular, peer review offers the benefit of eliminating papers that suffer from poor craftsmanship or methodological shortcomings, especially in the experimental sciences. However, we believe that peer review is not always appropriate for the evaluation of controversial hypothetical science. We argue that (...) the process of peer review can be prone to bias towards ideas that affirm the prior convictions of reviewers and against innovation and radical new ideas. Innovative hypotheses are thus highly vulnerable to being “filtered out” or made to accord with conventional wisdom by the peer review process. Consequently, having introduced peer review, the Elsevier journal Medical Hypotheses may be unable to continue its tradition as a radical journal allowing discussion of improbable or unconventional ideas. Hence we conclude by asking the publisher to consider re-introducing the system of editorial review to Medical Hypotheses. (shrink)

ABSTRACTInitial evaluations generalise to new contexts, whereas counter-attitudinal evaluations are context-specific. Counter-attitudinal information may not change evaluations in new contexts beca...

© 2017. The American Astronomical Society. All rights reserved.We present new observations of the low-mass companion to HD 984 taken with the Gemini Planet Imager as a part of the GPI Exoplanet Survey campaign. Images of HD 984 B were obtained in the J and H bands. Combined with archival epochs from 2012 and 2014, we fit the first orbit to the companion to find an 18 au orbit with a 68% confidence interval between 14 and 28 au, an eccentricity (...) of 0.18 with a 68% confidence interval between 0.05 and 0.47, and an inclination of 119°with a 68% confidence interval between 114°and 125°. To address the considerable spectral covariance in both spectra, we present a method of splitting the spectra into low and high frequencies to analyze the spectral structure at different spatial frequencies with the proper spectral noise correlation. Using the split spectra, we compare them to known spectral types using field brown dwarf and low-mass star spectra and find a best-fit match of a field gravity M6.5 ±1.5 spectral type with a corresponding temperature of K. Photometry of the companion yields a luminosity of log=2.88 ± 0.07 dex with DUSTY models. Mass estimates, again from DUSTY models, find an age-dependent mass of 34 ±1 to 95 ±4 M Jup. These results are consistent with previous measurements of the object. (shrink)