The low numbers of women and underrepresented minorities in engineering has often been characterized as a ‘pipeline problem,’ wherein few members of these groups choose engineering majors or ‘leak out’ of the engineering education pipeline before graduating [1]. Within this view, the difficulty of diversifying the engineering workforce can be addressed by stocking the pipeline with more diverse applicants.
However, the assumption that adding more underrepresented applicants will solve the complex and persistent issues of diversity and inclusion within engineering has been challenged by recent research. Studies of engineering culture highlight how the persistence of women and minorities is linked to norms and assumptions of engineering cultures (e.g., [2], [3]). For example, some engineering cultures have been characterized as masculine, leading women to feel that they must become ‘one of the guys’ to fit in and be successful (e.g., [4]). In the U.S., engineering cultures are also predominantly white, which can make people of color feel unwelcome or isolated [5]. When individuals feel unwelcome in engineering cultures, they are likely to leave. Thus, engineering culture plays an important role in shaping who participates and successfully persists in engineering education and practice.
Likewise, disciplinary cultures in engineering education also carry assumptions about what resources students should possess and utilize throughout their professional development. For example, educational cultures may assume students possess certain forms of ‘academic capital,’ such as rigorous training in STEM subjects prior to college. They might also assume students possess ‘navigational capital,’ or the ability to locate and access resources in the university system. However, these cultural assumptions have implications for the diversity and inclusivity of educational environments, as they shape what kinds of students are likely to succeed. For instance, first generation college (FGC) students may not possess the same navigational capital as continuing generation students [5]. Under-represented minority (URM) students often receive less pre-college training in STEM than their white counterparts [6]. However, FGC and URM students possess many forms of capital that often are unrecognized by education systems, for example, linguistic capital, or the ability to speak in multiple languages and styles) [7], [8]. Educational cultures that assume everyone possesses the same kinds of capital (i.e. that of white, American, high SES, and continuing generation students) construct barriers for students from diverse backgrounds. Thus, we propose that examining culture is essential for understanding the underlying assumptions and beliefs that give rise to the challenging issues surrounding the lack of diversity and inclusion in engineering.
This case study examines the culture of a biomedical engineering (BME) program at a large Midwestern university and identifies underlying assumptions regarding what sources of cultural and social capital undergraduate students need to be successful. By tracing when and how students draw upon these forms of capital during their professional development, we examine the implications for students from diverse backgrounds, particularly FGC and URM students.
more »
« less
What 50 Million Drawings Can Tell Us About Shared Meaning
A foundational assumption of linguistic communication is that conversants have similar underlying concepts (Brennan & Clark, 1996; Wierzbicka, 2012). On this view, the ability of one person to understand another when she says “the tree” depends on the word activating the same concept in both people. One approach to verifying this assumption is to rely on definitions, but this reasoning is circular— how can we be sure the words in our definitions are the same? Here, we investigate the assumption of shared linguistic concepts by studying concepts represented in the visual modality—drawings—and examining predictors of their variability. Specifically, we ask whether people who are geographically closer and inhabit a similar linguistic environment produce more similar drawings.
more »
« less
- Award ID(s):
- 1734260
- NSF-PAR ID:
- 10074364
- Date Published:
- Journal Name:
- Proceedings of the 12th International Conference on the Evolution of Language (Evolang12)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
The computer science literature on identification of people using personal information paints a wide spectrum, from aggregate information that doesn’t contain information about individual people, to information that itself identifies a person. However, privacy laws and regulations often distinguish between only two types, often called personally identifiable information and de-identified information. We show that the collapse of this technological spectrum of identifiability into only two legal definitions results in the failure to encourage privacy-preserving practices. We propose a set of legal definitions that spans the spectrum. We start with anonymous information. Computer science has created anonymization algorithms, including differential privacy, that provide mathematical guarantees that a person cannot be identified. Although the California Consumer Privacy Act (CCPA) defines aggregate information, it treats aggregate information the same as de-identified information. We propose a definition of anonymous information based on the technological possibility of logical association of the information with other information. We argue for the exclusion of anonymous information from notice and consent requirements. We next consider de-identified information. Computer science has created de-identification algorithms, including generalization, that minimize (but not eliminate) the risk of re-identification. GDPR defines anonymous information but not de-identified information, and CCPA defines de-identified information but not anonymous information. The definitions do not align. We propose a definition of de-identified information based on the reasonableness of association with other information. We propose legal controls to protect against re-identification. We argue for the inclusion of de-identified information in notice requirements, but the exclusion of de-identified information from choice requirements. We next address the distinction between trackable and non-trackable information. Computer science has shown how one-time identifiers can be used to protect reasonably linkable information from being tracked over time. Although both GDPR and CCPA discuss profiling, neither formally defines it as a form of personal information, and thus both fail to adequately protect against it. We propose definitions of trackable information and non-trackable information based on the likelihood of association with information from other contexts. We propose a set of legal controls to protect against tracking. We argue for requiring stronger forms of user choice for trackable information, which will encourage the use of non-trackable information. Finally, we address the distinction between pseudonymous and reasonably identifiable information. Computer science has shown how pseudonyms can be used to reduce identification. Neither GDPR nor CCPA makes a distinction between pseudonymous and reasonable identifiable information. We propose definitions based on the reasonableness of identifiability of the information, and we propose a set of legal controls to protect against identification. We argue for requiring stronger forms of user choice for reasonably identifiable information, which will encourage the use of pseudonymous information. Our definitions of anonymous information, de-identified information, non-trackable information, trackable information, and reasonably identifiable information can replace the over-simplified distinction between personally identifiable information versus de-identified information. We hope that this full spectrum of definitions can be used in a comprehensive privacy law to tailor notice and consent requirements to the characteristics of each type of information.more » « less
-
How do children’s representations of object categories change as they grow older? As they learn about the world around them, they also express what they know in the drawings they make. Here, we examine drawings as a window into how children represent familiar object categories, and how this changes across childhood. We asked children (age 3-10 years) to draw familiar object categories on an iPad. First, we analyzed their semantic content, finding large and consistent gains in how well children could produce drawings that are recognizable to adults. Second, we quantified their perceptual similarity to adult drawings using a pre-trained deep convolutional neural network, allowing us to visualize the representational layout of object categories across age groups using a common feature basis. We found that the organization of object categories in older children’s drawings were more similar to that of adults than younger children’s drawings. This correspondence was strong in the final layers of the neural network, showing that older children’s drawings tend to capture the perceptual features critical for adult recognition. We hypothesize that this improvement reflects increasing convergence between children’s representations of object categories and that of adults; future work will examine how these age-related changes relate to children’s developing perceptual and motor capacities. Broadly, these findings point to drawing as a rich source of insight into how children represent object concepts.more » « less
-
Symmetry is ubiquitous in nature, in logic and mathematics, and in perception, language, and thought. Although humans are exquisitely sensitive to visual symmetry (e.g., of a butterfly), symmetry in natural language goes beyond visuospatial properties: many words point to abstract concepts with symmetrical content (e.g., equal, marry). For example, if Mark marries Bill, then Bill marries Mark. In both cases (vision and language), symmetry may be formally characterized as invariance under transformation. Is this a coincidence, or is there some deeper psychological resemblance? Here we asked whether representations of symmetry correspond across language and vision. To do so, we developed a novel cross-modal matching paradigm. On each trial, participants observed a visual stimulus (either symmetrical or nonsymmetrical) and had to choose between a symmetrical and nonsymmetrical English predicate unrelated to the stimulus (e.g., “negotiate” vs. “propose”). In a first study with visual events (symmetrical collision or asymmetrical launch), participants reliably chose the predicate matching the event’s symmetry. A second study showed that this “language-vision correspondence” generalized to objects and was weakened when the stimuli’s binary nature was made less apparent (i.e., for one object, rather than two inward-facing objects). A final study showed the same effect when nonsigners guessed English translations of signs from American Sign Language, which expresses many symmetrical concepts spatially. Taken together, our findings support the existence of an abstract representation of symmetry which humans access via both perceptual and linguistic means. More broadly, this work sheds light on the rich, structured nature of the language-cognition interface.more » « less
-
Symmetry is ubiquitous in nature, in logic and mathematics, and in perception, language, and thought. Although humans are exquisitely sensitive to visual symmetry (e.g., of a butterfly), symmetry in natural language goes beyond visuospatial properties: many words point to abstract concepts with symmetrical content (e.g., equal, marry). For example, if Mark marries Bill, then Bill marries Mark. In both cases (vision and language), symmetry may be formally characterized as invariance under transformation. Is this a coincidence, or is there some deeper psychological resemblance? Here we asked whether representations of symmetry correspond across language and vision. To do so, we developed a novel cross-modal matching paradigm. On each trial, participants observed a visual stimulus (either symmetrical or non-symmetrical) and had to choose between a symmetrical and non-symmetrical English predicate unrelated to the stimulus (e.g., “negotiate” vs. “propose”). In a first study with visual events (symmetrical collision or asymmetrical launch), participants reliably chose the predicate matching the event’s symmetry. A second study showed that this “language-vision correspondence” generalized to objects, and was weakened when the stimuli’s binary nature was made less apparent (i.e., for one object, rather than two inward-facing objects). A final study showed the same effect when nonsigners guessed English translations of signs from American Sign Language, which expresses many symmetrical concepts spatially. Taken together, our findings support the existence of an abstract representation of symmetry which humans access via both perceptual and linguistic means. More broadly, this work sheds light on the rich, structured nature of the language-cognition interface.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.12775/3991-1.059
