Thesis (Ph. D.)--University of Wisconsin--Madison, 1969. / Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references.

Thesis (Ed. D.)--Illinois State University, 1995. / Title from title page screen, viewed May 10, 2006. Dissertation Committee: Dent M. Rhodes (chair), Larry Kennedy, Kenneth Jerrich, Frederick Drake. Includes bibliographical references (leaves 195-204) and abstract. Also available in print.

Thesis (Ph. D.)--University of Washington, 2007. / Vita. Includes bibliographical references (leaves 200-217).

The contemporary animal rights movement, in extending moral consideration to nonhuman animals, has in diverse ways already contributed to an expansion of the boundaries of the ethical community and what that may constitute. However, many environmentalists argue there is a failure within animal ethics to adequately address wider animal advocacy concerns, and that consideration of broader ecosystemic challenges elicit at best moot response from mainstream animal rights advocates. In taking an individualistically based biocentric approach, the essential aims of animal ethics can, I argue, be readily embraced into a theory of value that can address this wider remit. In aligning the applicability of a developed form of biocentric individualism with the ethical underpinning of notions of the 'illegitimacy of animal use' extrapolated from normative animal advocacy perspectives, my proposition is that these shortcomings can be ameliorated and that such an alignment forms a complimentary and useful fusion. Biocentrism as a value theory asks for moral considerability to be centred upon a respect for individual nonhuman (and human) life and the possession/continuation of a flourishing individual life - neither of which, I contend, is at odds with the essential spirit of animal ethics. In this sense, I submit that a developed biocentric individualism 'bridges the gap' between animal ethics and environmental ethics.

Fearful that colleges students today are 40% lower in empathy than colleges students 20 years ago (Komives, 2017), Dr. Carson Medley created the PEAKS (Persistence, Empathy, Authenticity, Knowledge, Skills) course. As a culminating activity, the class will turn Carter Hall into the first-ever nationwide college campus and student-led Empathy Museum for a day based on different experiential learning experiences offered throughout the semester. Each group of students will be responsible for turning one of their experiences into an exhibit at the Honors College Empathy Museum. Each exhibit will require an interview and research about the issue. The entire campus (students, staff, faculty, and administrators) will be invited to participate in the interactive empathy museum. The curators (a chemistry major and a microbiology major) for the museum are Changemaker Scholars. The Changemaker program curriculum focuses on human rights, global awareness, and global engagement through the lens of volunteering and community engagement. The purpose of this Changemaker Capstone is to inspire others around the world to create their own Empathy Museums to address xenophobia, Black Lives Matter, gender inequities, immigration policies, LGTBQI+ rights, politics, global warming, the Covid-19 pandemic, and others. We believe that these empathy-focused exhibits will empower and affirm spaces on college campuses as restorative environments and reiterate the healing power of beauty and culture.

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Depression is a crippling burden on a great many people, and it is often well hidden. Mental health professionals are able to treat depression, but the general public is not well versed in recognizing depression symptoms or assessing their own mental health. Active Listening Journal Interaction (ALJI) is a computer program that seeks to identify and refer people suffering with depression to mental health support services. It does this through analyzing personal journal entries using machine learning, and then privately responding to the author with proper guidance. In this thesis, we focus on determining the feasibility and usefulness of the machine learning models that drive ALJI. With heavy data limitations, we cautiously report that with a single journal entry, our model detects when a person's symptoms warrant professional intervention with a 61% accuracy. A great amount of discussion on the proposed solution, methods, results, and future directions of ALJI is included. / Master of Science / An incredibly large number of people suffer from depression, and they can rightfully feel trapped or imprisoned by this illness. A very simple way to understand depression is to first imagine looking at the most beautiful sunset you've ever seen, and then imagine feeling absolutely nothing while looking that same sunset, and you can't explain why. When a person is depressed, they are likely to feel like a burden to those around them. This causes them to avoid social gathering and friends, making them isolated away from people that could support them. This worsens their depression and a terrible cycle begins. One of the best ways out of this cycle is to reveal the depression to a doctor or psychologist, and to ask them for guidance. However, many people don't see or realize this excellent option is open to them, and will continue to suffer with depression for far longer than needed. This thesis describes an idea called the Active Listening Journal Interaction, or ALJI. ALJI acts just like someone's personal journal or diary, but it also has some protections from illnesses like depression. First, ALJI searches a journal entry for indicators about the author's health, then ALJI asks the author a few questions to better understand the author, and finally ALJI gives that author information and guidance on improving their health. We are starting to create a computer program of ALJI by first building and testing the detector for the author's health. Instead of making the detector directly, we show the computer some examples of the health indicators from journals we know very well, and then let the computer focus on finding the pattern that would reveal those health indicators from any journal. This is called machine learning, and in our case, ALJI's machine learning is going to be difficult because we have very few example journals where we know all of the health indicators. However, we believe that fixing this issue would solve the first step of ALJI. The end of this thesis also discusses the next steps going forward with ALJI.