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This dissertation addresses empirical, applied, and theoretical issues in the place literature through an ethnographic study of the volunteer stewards in the nonprofit McDowell Sonoran Conservancy (Scottsdale, Arizona).
The first phase of study explores Conservancy stewards’ phenomenological place meanings through participant observation, a photovoice protocol (N=18), and life-history interviews (N=53). Findings indicate that being a steward fosters deep, identity-based place meanings within the conservation land (the McDowell Sonoran Preserve) and City of Scottsdale.
The second phase of study measures stewards’ psychometric place attachments to the Preserve and broader community using the Place Attachment Inventory (PAI) survey. New stewards’ (N=29) PAI scores—collected before attending orientation and one year after—demonstrate a rise in Preserve place attachment and place identity in the first year of service. Established stewards’ (N=275) PAI data suggests no correlation between place attachment and volunteer intensity. These findings are complemented by phase I results and suggest that stewards experience a rise in place identity after earning the identity of an environmental steward, regardless of engagement.
The third phase of study experimentally combines the data from established stewards who participated in phase I and II (N=48) to test the hypothesis that those with identity-based place meanings would possess higher place identity scores. Data analysis found no significant differences in place identity scores between those with and without a Predicted High Place Identity. The outcomes of this experiment suggest construct validity issues with the widely used place attachment and place identity constructs.
While it is established that volunteers arrive at an organization with a strong sense of place, this study demonstrates empirically how place attachments increase and place meanings deepen further after joining a volunteer organization. Communities and organizations can learn from the Conservancy’s practices that help stewards easily establish and perform a place-based steward identity. Finally, the experimental mixed methods findings suggest a sense of place research program that measures attachment to a place’s meanings rather than attachment to a place. This shift will allow place meaning and place attachment to be studied concurrently, advancing the sense of place construct and broader place theory.
The first phase of study explores Conservancy stewards’ phenomenological place meanings through participant observation, a photovoice protocol (N=18), and life-history interviews (N=53). Findings indicate that being a steward fosters deep, identity-based place meanings within the conservation land (the McDowell Sonoran Preserve) and City of Scottsdale.
The second phase of study measures stewards’ psychometric place attachments to the Preserve and broader community using the Place Attachment Inventory (PAI) survey. New stewards’ (N=29) PAI scores—collected before attending orientation and one year after—demonstrate a rise in Preserve place attachment and place identity in the first year of service. Established stewards’ (N=275) PAI data suggests no correlation between place attachment and volunteer intensity. These findings are complemented by phase I results and suggest that stewards experience a rise in place identity after earning the identity of an environmental steward, regardless of engagement.
The third phase of study experimentally combines the data from established stewards who participated in phase I and II (N=48) to test the hypothesis that those with identity-based place meanings would possess higher place identity scores. Data analysis found no significant differences in place identity scores between those with and without a Predicted High Place Identity. The outcomes of this experiment suggest construct validity issues with the widely used place attachment and place identity constructs.
While it is established that volunteers arrive at an organization with a strong sense of place, this study demonstrates empirically how place attachments increase and place meanings deepen further after joining a volunteer organization. Communities and organizations can learn from the Conservancy’s practices that help stewards easily establish and perform a place-based steward identity. Finally, the experimental mixed methods findings suggest a sense of place research program that measures attachment to a place’s meanings rather than attachment to a place. This shift will allow place meaning and place attachment to be studied concurrently, advancing the sense of place construct and broader place theory.
ContributorsBleam, Ryan M (Author) / BurnSilver, Shauna (Thesis advisor) / Tsuda, Takeyuki (Committee member) / Brandt, Elizabeth, (Committee member) / Toon, Richard (Committee member) / Arizona State University (Publisher)
Created2020
Description
The United States accounts for only 4% of the world’s female population, but it is home to more than 30% of the world’s incarcerated women, the majority of whom will eventually attempt a successful reentry into society. Almost half of the incarcerated women in the United States have not obtained a high school diploma or equivalency, and only 31% have attempted some college, compared to 58% among the general public (Ewert & Wildhagen, 2011). There is ample evidence of the impact of a post-secondary degree on reducing recidivism and increasing reentry success. However, the Arizona Department of Corrections reports that of the more than 40,000 people incarcerated in November of 2019, only 5,333, or 12.5%, were involved in any type of educational programming while incarcerated (2019).
Few studies have looked closely at the barriers to higher education for formerly incarcerated individuals, and even fewer have focused on women. The purpose of this qualitative action research study was to examine the educational experiences of formerly incarcerated women through the lenses of critical social theory (Freeman & Vasconcelos, 2010; Freire, 1970) and possible selves theory (Markus & Nurius, 1986) in an effort to more fully understand low educational attainment in this population and use this knowledge to develop an effective, participant-informed intervention and provide recommendations for university outreach programs. Study participants were formerly incarcerated women and individuals who work with this population. Data were collected from in-depth semi-structured interviews and materials created during the College After Prison Workshop which was developed for this project.
Interviews revealed that the women in this study crave a sense of belonging, feel regret over their lost possible selves, experience a fear of standing still or going backward, and have a strong desire to help others. Findings suggest that colleges and universities can support formerly incarcerated women in the post-secondary system by curating a community of scholars and demonstrating a clear path forward for formerly incarcerated women by reducing systemic barriers.
Few studies have looked closely at the barriers to higher education for formerly incarcerated individuals, and even fewer have focused on women. The purpose of this qualitative action research study was to examine the educational experiences of formerly incarcerated women through the lenses of critical social theory (Freeman & Vasconcelos, 2010; Freire, 1970) and possible selves theory (Markus & Nurius, 1986) in an effort to more fully understand low educational attainment in this population and use this knowledge to develop an effective, participant-informed intervention and provide recommendations for university outreach programs. Study participants were formerly incarcerated women and individuals who work with this population. Data were collected from in-depth semi-structured interviews and materials created during the College After Prison Workshop which was developed for this project.
Interviews revealed that the women in this study crave a sense of belonging, feel regret over their lost possible selves, experience a fear of standing still or going backward, and have a strong desire to help others. Findings suggest that colleges and universities can support formerly incarcerated women in the post-secondary system by curating a community of scholars and demonstrating a clear path forward for formerly incarcerated women by reducing systemic barriers.
ContributorsBell, Kendra (Author) / Judson, Eugene (Thesis advisor) / Dinn-You Liou, Daniel (Committee member) / Tsoudis, Olga (Committee member) / Arizona State University (Publisher)
Created2020
Description
This research project will focus on two poems by the Korean poet So-wol Kim (1902-1934). His poems are admired throughout Korea and are often set by Korean art song composers. This paper will examine four art song settings by composers Sung-tae Kim (1910-2012) and Soon-nam Kim (1917-1986) of two poems by So-wol Kim: “Azalea” and “Wildflowers of the Mountains.” The discussion will examine in detail the varied interpretations and expressions of the texts by each composer. To be clear, the translations of the poems investigated in this paper are poetic renderings and are not meant for performance purposes.
ContributorsSeo, Juhee (Author) / Mills, Robert (Thesis advisor) / Norton, Kay (Committee member) / Rockmaker, Jody (Committee member) / Arizona State University (Publisher)
Created2020
Description
The “filter bubble” has been a heated discussion topic since several years ago. In addition to possible algorithmic contribution to this phenomenon, people’s selective exposure tendency may be another primary cause of the “filter bubble” on social media. Prior research indicates that, under the influence of selective exposure tendency, people tend to perceive pro-attitudinal news as more credible than counter-attitudinal news, with strong partisans more likely to be affected. The proposed thesis seeks to examine whether the perceived credibility of a news source and story on social media is influenced by selective exposure and strength of partisanship. Through an experimental study via Amazon’s Mechanical Turk, 468 participants chose or were assigned to read an ostensible news story from a social media feed with the news source and ideological slant varied between participants. The results showed that people reported higher perceived source and story credibility when the source and stories were pro-attitudinal (consistent with their political ideology) as opposed to counter-attitudinal, regardless of participants’ age, race, perceived credibility of news from social media, in general, and strength of partisanship. However, contrary to the hypotheses, selective exposure behavior (i.e., choosing a preferred news source before reading a news story) did not affect credibility perceptions when participants read counter-attitudinal news from a pro-attitudinal source. Last, strength of partisanship did not moderate the influence of selective exposure on credibility perceptions. In sum, this study suggests that although selective exposure tendency may affect people’s credibility perceptions and contribute to “filter bubbles,” the impact of selective exposure behavior may be overestimated in terms of perceived source and story credibility of news on social media.
ContributorsLiu, Xingyu (Author) / Mickelson, Kristin D (Thesis advisor) / Hall, Deborah (Committee member) / Walker, Shawn (Committee member) / Arizona State University (Publisher)
Created2020
Description
Shock loading produces a compressive stress pulse with steep gradients in density, temperature, and pressure that are also often modeled as discontinuities. When a material is subject to these dynamic (shock) loading conditions, fracture and deformation patterns due to spall damage can arise. Spallation is a dynamic material failure that is caused by the nucleation, growth, and coalescence of voids, with possible ejection of the surface of the material. Intrinsic defects, such as grain boundaries are the preferred initiation sites of spall damage in high purity materials. The focus of this research is to study the phenomena that cause void nucleation and growth at a particular grain boundary (GB), chosen to maximize spall damage localization.
Bicrystal samples were shock loaded using flyer-plates via light gas gun and direct laser ablation. Stress, pulse duration, and crystal orientation along the shock direction were varied for a fixed boundary misorientation to determine thresholds for void nucleation and coalescence as functions of these parameters. Pressures for gas gun experiments ranged from 2 to 5 GPa, while pressures for laser ablation experiments varied from 17 to 25 GPa. Samples were soft recovered to perform damage characterization using electron backscattering diffraction (EBSD) and Scanning Electron Microscopy (SEM). Results showed a 14% difference in the thresholds for void nucleation and coalescence between samples with different orientations along the shock direction, which were affected by pulse duration and stress level. Fractography on boundaries with strong damage localization showed many small voids, indicating they experience rapid nucleation, causing early coalescence. Composition analysis was also performed to determine the effect of impurities on damage evolution. Results showed that higher levels of impurities led to more damage. ABAQUS/Explicit models were developed to simulate flyer-plate impact and void growth with the same crystal orientations and experimental conditions. Results are able to match the damage seen in each grain of the target experimentally. The Taylor Factor mismatch at the boundary can also be observed in the model with the higher Taylor Factor grain exhibiting more damage.
Bicrystal samples were shock loaded using flyer-plates via light gas gun and direct laser ablation. Stress, pulse duration, and crystal orientation along the shock direction were varied for a fixed boundary misorientation to determine thresholds for void nucleation and coalescence as functions of these parameters. Pressures for gas gun experiments ranged from 2 to 5 GPa, while pressures for laser ablation experiments varied from 17 to 25 GPa. Samples were soft recovered to perform damage characterization using electron backscattering diffraction (EBSD) and Scanning Electron Microscopy (SEM). Results showed a 14% difference in the thresholds for void nucleation and coalescence between samples with different orientations along the shock direction, which were affected by pulse duration and stress level. Fractography on boundaries with strong damage localization showed many small voids, indicating they experience rapid nucleation, causing early coalescence. Composition analysis was also performed to determine the effect of impurities on damage evolution. Results showed that higher levels of impurities led to more damage. ABAQUS/Explicit models were developed to simulate flyer-plate impact and void growth with the same crystal orientations and experimental conditions. Results are able to match the damage seen in each grain of the target experimentally. The Taylor Factor mismatch at the boundary can also be observed in the model with the higher Taylor Factor grain exhibiting more damage.
ContributorsFortin, Elizabeth Victoria (Author) / Peralta, Pedro (Thesis advisor) / Mignolet, Marc (Committee member) / Loomis, Eric (Committee member) / Oswald, Jay (Committee member) / Solanki, Kiran (Committee member) / Arizona State University (Publisher)
Created2020
Description
Modern digital applications have significantly increased the leakage of private and sensitive personal data. While worst-case measures of leakage such as Differential Privacy (DP) provide the strongest guarantees, when utility matters, average-case information-theoretic measures can be more relevant. However, most such information-theoretic measures do not have clear operational meanings. This dissertation addresses this challenge.
This work introduces a tunable leakage measure called maximal $\alpha$-leakage which quantifies the maximal gain of an adversary in inferring any function of a data set. The inferential capability of the adversary is modeled by a class of loss functions, namely, $\alpha$-loss. The choice of $\alpha$ determines specific adversarial actions ranging from refining a belief for $\alpha =1$ to guessing the best posterior for $\alpha = \infty$, and for the two specific values maximal $\alpha$-leakage simplifies to mutual information and maximal leakage, respectively. Maximal $\alpha$-leakage is proved to have a composition property and be robust to side information.
There is a fundamental disjoint between theoretical measures of information leakages and their applications in practice. This issue is addressed in the second part of this dissertation by proposing a data-driven framework for learning Censored and Fair Universal Representations (CFUR) of data. This framework is formulated as a constrained minimax optimization of the expected $\alpha$-loss where the constraint ensures a measure of the usefulness of the representation. The performance of the CFUR framework with $\alpha=1$ is evaluated on publicly accessible data sets; it is shown that multiple sensitive features can be effectively censored to achieve group fairness via demographic parity while ensuring accuracy for several \textit{a priori} unknown downstream tasks.
Finally, focusing on worst-case measures, novel information-theoretic tools are used to refine the existing relationship between two such measures, $(\epsilon,\delta)$-DP and R\'enyi-DP. Applying these tools to the moments accountant framework, one can track the privacy guarantee achieved by adding Gaussian noise to Stochastic Gradient Descent (SGD) algorithms. Relative to state-of-the-art, for the same privacy budget, this method allows about 100 more SGD rounds for training deep learning models.
This work introduces a tunable leakage measure called maximal $\alpha$-leakage which quantifies the maximal gain of an adversary in inferring any function of a data set. The inferential capability of the adversary is modeled by a class of loss functions, namely, $\alpha$-loss. The choice of $\alpha$ determines specific adversarial actions ranging from refining a belief for $\alpha =1$ to guessing the best posterior for $\alpha = \infty$, and for the two specific values maximal $\alpha$-leakage simplifies to mutual information and maximal leakage, respectively. Maximal $\alpha$-leakage is proved to have a composition property and be robust to side information.
There is a fundamental disjoint between theoretical measures of information leakages and their applications in practice. This issue is addressed in the second part of this dissertation by proposing a data-driven framework for learning Censored and Fair Universal Representations (CFUR) of data. This framework is formulated as a constrained minimax optimization of the expected $\alpha$-loss where the constraint ensures a measure of the usefulness of the representation. The performance of the CFUR framework with $\alpha=1$ is evaluated on publicly accessible data sets; it is shown that multiple sensitive features can be effectively censored to achieve group fairness via demographic parity while ensuring accuracy for several \textit{a priori} unknown downstream tasks.
Finally, focusing on worst-case measures, novel information-theoretic tools are used to refine the existing relationship between two such measures, $(\epsilon,\delta)$-DP and R\'enyi-DP. Applying these tools to the moments accountant framework, one can track the privacy guarantee achieved by adding Gaussian noise to Stochastic Gradient Descent (SGD) algorithms. Relative to state-of-the-art, for the same privacy budget, this method allows about 100 more SGD rounds for training deep learning models.
ContributorsLiao, Jiachun (Author) / Sankar, Lalitha (Thesis advisor) / Kosut, Oliver (Committee member) / Zhang, Junshan (Committee member) / Dasarathy, Gautam (Committee member) / Arizona State University (Publisher)
Created2020
Description
Few studies bridge workplace engagement and employee voice with internal communication. This analysis builds upon both the crucial concept of employee engagement and its implications for communication professionals and leaders. Further, it calls for more strategic integration of upward employee voice in internal communications. By examining factors that support communication (in two directions) and especially upward employee voice, researchers examine a case study of an intranet site at a major academic research institute. Factors that support employee expression are compared with data streams from both user survey and website analytics. The results point to voice-inducing techniques include projecting critical mass, fostering trust, and emphasizing intranet usefulness and rewards. By enriching workplace communications, voice can strengthen the employee’s ability to contribute, connect leaders with a source for direct feedback, and help employers be more responsive and nimbler.
ContributorsKurth, Julie (Author) / Maid, Barry (Thesis advisor) / Brumberger, Eva (Committee member) / D'Angelo, Barbara (Committee member) / Arizona State University (Publisher)
Created2020
Description
In a multi-robot system, locating a team robot is an important issue. If robots
can refer to the location of team robots based on information through passive action
recognition without explicit communication, various advantages (e.g. improving security
for military purposes) can be obtained. Specifically, when team robots follow
the same motion rule based on information about adjacent robots, associations can
be found between robot actions. If the association can be analyzed, this can be a clue
to the remote robot. Using these clues, it is possible to infer remote robots which are
outside of the sensor range.
In this paper, a multi-robot system is constructed using a combination of Thymio
II robotic platforms and Raspberry pi controllers. Robots moving in chain-formation
take action using motion rules based on information obtained through passive action
recognition. To find associations between robots, a regression model is created using
Deep Neural Network (DNN) and Long Short-Term Memory (LSTM), one of state-of-art technologies.
The input data of the regression model is divided into historical data, which
are consecutive positions of the robot, and observed data, which is information about the
observed robot. Historical data is sequence data that is analyzed through the LSTM
layer. The accuracy of the regression model designed using DNN can vary depending
on the quantity and quality of the input. In this thesis, three different input situations
are assumed for comparison. First, the amount of observed data is different, second, the
type of observed data is different, and third, the history length is different. Comparative
models are constructed for each case, and prediction accuracy is compared to analyze
the effect of input data on the regression model. This exploration validates that these
methods from deep learning can reduce the communication demands in coordinated
motion of multi-robot systems
can refer to the location of team robots based on information through passive action
recognition without explicit communication, various advantages (e.g. improving security
for military purposes) can be obtained. Specifically, when team robots follow
the same motion rule based on information about adjacent robots, associations can
be found between robot actions. If the association can be analyzed, this can be a clue
to the remote robot. Using these clues, it is possible to infer remote robots which are
outside of the sensor range.
In this paper, a multi-robot system is constructed using a combination of Thymio
II robotic platforms and Raspberry pi controllers. Robots moving in chain-formation
take action using motion rules based on information obtained through passive action
recognition. To find associations between robots, a regression model is created using
Deep Neural Network (DNN) and Long Short-Term Memory (LSTM), one of state-of-art technologies.
The input data of the regression model is divided into historical data, which
are consecutive positions of the robot, and observed data, which is information about the
observed robot. Historical data is sequence data that is analyzed through the LSTM
layer. The accuracy of the regression model designed using DNN can vary depending
on the quantity and quality of the input. In this thesis, three different input situations
are assumed for comparison. First, the amount of observed data is different, second, the
type of observed data is different, and third, the history length is different. Comparative
models are constructed for each case, and prediction accuracy is compared to analyze
the effect of input data on the regression model. This exploration validates that these
methods from deep learning can reduce the communication demands in coordinated
motion of multi-robot systems
ContributorsKang, Sehyeok (Author) / Pavlic, Theodore P (Thesis advisor) / Richa, Andréa W. (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2020
Description
One of the theoretical cores and values of good governance is the accountability of public employees, where the citizens expect the public employees to maintain professional standards, avoid conflicts of interest, respect the principles of fair and impartial treatment, and use public money wisely. However, are these unique moral standards to which only public employees are held? The dissertation seeks to examine how the public evaluates the unethical behaviors of public and private leaders differently to better understand the sources of public and private sector differences in the public’s normative evaluations.
Based on a randomized online vignette experiment with 1,569 respondents residing in the United States collected in Amazon’s Mechanical Turk platform, the dissertation confirms that public authorities face different levels of public tolerance relative to business managers. More specifically, the unethical behaviors of a public manager are less likely to be tolerated than the same misconduct of a business manager, while ethical offenses of elected officials are least likely to be tolerated by the public. However, the public is relatively much less tolerant of public managers’ and elected officials’ petty violations relative to business managers than they do for more egregious violations of public authorities.
The dissertation further finds that public evaluations are contingent upon the respondents’ work experience in different sectors. Individuals working in government are more likely to be tolerant of petty unethical behaviors, regardless of whom they evaluate, but they become much less tolerant of public managers’ and elected officials’ grand ethical violations. The longer individuals work in for-profit organizations, the less likely they are to tolerate public authorities’ petty violations of organizational rules while consistently being more accepting of the unethical behaviors of business managers.
Using an experimental design, the dissertation finds the importance of a fair and legitimate use of tax money in the public’s moral evaluations of public leadership and further discusses the potential sources of public skepticism of the public sector. Furthermore, the public and private sector comparison provides theoretical and practical implications for ethics reform in the era of collaborative governance.
Based on a randomized online vignette experiment with 1,569 respondents residing in the United States collected in Amazon’s Mechanical Turk platform, the dissertation confirms that public authorities face different levels of public tolerance relative to business managers. More specifically, the unethical behaviors of a public manager are less likely to be tolerated than the same misconduct of a business manager, while ethical offenses of elected officials are least likely to be tolerated by the public. However, the public is relatively much less tolerant of public managers’ and elected officials’ petty violations relative to business managers than they do for more egregious violations of public authorities.
The dissertation further finds that public evaluations are contingent upon the respondents’ work experience in different sectors. Individuals working in government are more likely to be tolerant of petty unethical behaviors, regardless of whom they evaluate, but they become much less tolerant of public managers’ and elected officials’ grand ethical violations. The longer individuals work in for-profit organizations, the less likely they are to tolerate public authorities’ petty violations of organizational rules while consistently being more accepting of the unethical behaviors of business managers.
Using an experimental design, the dissertation finds the importance of a fair and legitimate use of tax money in the public’s moral evaluations of public leadership and further discusses the potential sources of public skepticism of the public sector. Furthermore, the public and private sector comparison provides theoretical and practical implications for ethics reform in the era of collaborative governance.
ContributorsJung, Jiwon (Author) / Bozeman, Barry (Thesis advisor) / Bretschneider, Stuart (Committee member) / Corley, Elizabeth (Committee member) / Arizona State University (Publisher)
Created2020
Description
Recent research finds that there is significant variation in stock market participation by state and suggests that there might be state-specific factors that determine household stock market participation in the United States. Using household survey data, I examine how accounting quality of public companies at the state level affects households’ stock market participation decisions. I find that households residing in states where local public companies have better accounting quality are more likely to invest in stocks. Moreover, those households invest greater amounts of their wealth in the stock market. Cross-sectional tests find that the effect of accounting quality on stock market participation is more pronounced for less affluent and less educated households, consistent with prior findings that lacking familiarity with and trust in the stock market is an important factor deterring those types of households from stock investments. In state-level tests, I find that these household outcomes affect income inequality, which is less severe in states where high public-firm accounting quality spurs more stock market participation by poorer households. Conversely, in states where public firms have lower accounting quality, stock market participation among poorer households is less common, and a larger share of high equity returns accrues to richer households, exacerbating income inequality.
ContributorsKim, Min (Author) / Huang, Xiaochuan (Thesis advisor) / Rykaczewski, Maria (Committee member) / White, Roger (Committee member) / Arizona State University (Publisher)
Created2020