ABOUT THE COLLECTIONS

The graduate and undergraduate research collections share, archive and preserve research from University of Arizona students. Collections include honors theses, master's theses, and dissertations, in addition to capstone and other specialized research and presentation topics.


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If you have questions about items in these collections, or are a faculty member who would like to provide students in your program an opportunity to showcase their research, please contact the Campus Repository team at repository@u.library.arizona.edu with your request. We look forward to working with you.

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Recent Submissions

  • Financial Instability

    Bustamante, Araceli; Chavez, Anita; Liebman, Maddy; Reynolds, Leah (The University of Arizona., 2022)
  • Job Security

    Ngomo, Kim; Dawson, Matilda; Torres, Rafael; Black, Sky (The University of Arizona., 2022)
  • Poverty in Tucson: Stress and Mental Health

    Hilyard, Raven; Lamadrid, Jimena; Grabow, Dylan; Wagner, Annie; Johnson, Mark (The University of Arizona., 2022)
  • Inflation

    Moya, Samantha; Hernandez, Vanessa; Foster, Emma; Michelman, Drew (The University of Arizona., 2022)
  • Housing Quality

    Broski, Catherine; Laehn, Emma; Strazzella, Sophie; Walter, Meg (The University of Arizona., 2022)
  • Housing Insecurity in Pima County

    Silva, Anthony; Delgadillo, Arlette; Leon, Dania; Nair, Vaisha (The University of Arizona., 2022)
  • Food Insecurity in Pima County

    Cortinas, Yasmine; Guan, Siyu; Montes, Camila; Schlangen, Grace (The University of Arizona., 2022)
  • The Continuing Impact of the Emergency Rental Assistance Program (ERAP) in Pima County in 2022: An Overview of Program Outcomes and Future Considerations

    Long, Seth; Matty, Alejandra; Holt, Gloria; Jimenez, Jadyn; Pongratz, Andrew (The University of Arizona., 2022)
  • Caregiving and Poverty

    Davidson, Ellie; Flinn, Allison; Yakpogoro, Yira; Sorrentino, Bianca; Sanchez, Mercy (The University of Arizona., 2022)
  • Cavitation and cavitation damage

    Rogers, W. L.; Wang, Shih-cheng, 1938- (The University of Arizona., 1965)
  • Stress-Induced Dynamics of FOXO Transcription Factors

    Paek, Andrew; Lasick, Kathleen Anne; El-Kareh, Ardith; Schroeder, Joyce; Weinert, Ted; Yao, Guang (The University of Arizona., 2023)
    The FOXO family of transcription factors have several important roles in multicellular organisms: they are required for proper development of different tissues, maintain homeostasis in response to diverse cellular stresses, function as tumor suppressors, and have an evolutionarily conserved role in prolonging lifespan. Consistent with their role in diverse cellular processes, FOXO proteins are activated by several different stimuli, leading to the promotion of many different downstream programs often with opposing outcomes. How FOXO protein activation can lead to stimulus-dependent transcriptional outcomes is not known, though several mechanisms have been proposed. Possible mechanisms include differences in FOXO post-translational modifications, binding partners, and the dynamics of FOXO activation. Here, I will describe the current evidence in the literature supporting these mechanisms, and our investigation into the role of dynamic patterns of activation of the FOXO transcription factors. Specifically, I set out to determine whether FOXO responds to different stresses with different temporal patterns of activation. I have shown that FOXO responds to oxidative stress in a sustained, bimodal pattern, while it responds to serum starvation in a stochastic pulsatile pattern. I also found that in MCF7 breast cancer cells, both patterns are controlled by the activity of the primary negative regulator of FOXO, Akt.
  • Monolithic Three-Mirror Anastigmat Telescope

    Kim, Daewook; Han, Yuqiao; Choi, Heejoo; Kupinski, Meredith (The University of Arizona., 2023)
    This thesis presents the design of a near-infrared (near-IR) three-mirror anastigmat telescope with a focal length of 38,675.1 ??, an entrance pupil diameter of 1,933.75 ??, and a 0.12° full field-of-view (FoV). This diffraction-limited telescope has a RMS spot radius of 36.6 μm (similar to the radius of Airy disk) to satisfy the design resolution requirements of the IR telescope. Designed specifically for near-IR sensing, the system benchmarked a widely known module of an IR detector, called the Near-Infrared Camera (NIRCam) detector, with ten Teledyne HgCdTe H2RG detectors. The NIRCam detector was assumed in this TMA design study, similar to the James Webb Space Telescope case. This three-mirror telescope design contains only two substrates, one consisting of two mirror surfaces with different radii of curvature. Consequently, the structure of this robust telescope design is straightforward and it is therefore easy to manufacture and align. The starting point for the optical design of this telescope was the Vera C. Rubin Observatory (a.k.a. Large Synoptic Survey Telescope), a three-mirror anastigmat system combined with more refractive lenses and a color filters. Importantly, the specifications of these three mirrors were changed to achieve the goal of aberration balancing. To determine the tolerances of the optical design, the M2 mirror substrate was misaligned with respect to the M1/M3 monolithic mirror, and the realistic maximum error was evaluated. As a result, in the range of 36.6 ?? root mean square (RMS) spot radius for the three degrees of freedom (DoF), the calculated tolerances were 0.0072° for X-rotation, 43 µ? for Y-translation, and 7.6 µ? for Z-translation. This result confirmed that the required machining accuracy to build the proposed telescope is within the advanced optical fabrication (e.g., diamond turning) and modern integration capabilities.
  • Efficacy of Chlorine and Peracetic Acid to Reduce Shiga Toxin-Producing Escherichia Coli and Impact of Simultaneous Nitrogen-Based Fertilizer Use on Microbial Die-Off in Preharvest Agricultural Water

    Rock, Channah; Scott, Zoe; Cooper, Kerry; Gerba, Charles (The University of Arizona., 2023)
    Several foodborne disease outbreaks in the United States have been linked to the consumption of various types of leafy greens in which irrigation water was suspected as the potential source of contamination. To reduce potential produce contamination from agricultural water, the U.S. Food and Drug Administration (FDA) has proposed regulations/metrics which would require growers to assess their agricultural water systems. In some cases, this would mean monitoring their water quality and taking corrective action, by way of antimicrobial treatments, when agricultural waters are deemed as a “reasonably likely foreseeable hazard”. Additionally, the Arizona and California Leafy Greens Marketing Agreements (AZ/CA LGMA) require growers utilizing surface water for overhead irrigation to treat their water within 21 days of harvest to meet acceptable risk indicators; generic Escherichia coli (E. coli) (non-detect per 100mL) and Total Coliform bacteria (<99 MPN/100mL). For many growers, this will be the first time that water quality data may necessitate them to use an antimicrobial treatment before irrigation can be applied safely. Additionally, growers are faced with a myriad of options related to antimicrobial water treatment with very little guidance on the most appropriate treatment option for their ranch, or the requirements needed for successful implementation. With limited guidance, water treatment decisions are likely to be unsuccessful and expend both excess time and money while seeing little to no reduction in potential pathogen loading in an agricultural water source and thus little to no reduction in microbiological risk. To provide guidance on antimicrobial agricultural water treatment options available to industry, the efficacy of two antimicrobial treatments Peroxyacetic Acid (PAA) and Calcium Hypochlorite (Cl) were tested, in triplicate. Tests were executed for various rates of each antimicrobial product (sanitizer), 6 & 8 PPM for PAA and 2 & 4 PPM for Cl. For each sanitizer at each PPM, tests were conducted at temperatures 12°C and 32°C. To evaluate sanitization efficacy, the team measured the reduction of a 109 CFU/mL cocktail of Shiga toxin-producing E. coli (STEC) strains (ATCC MP-9 and 43895) in four water sources from across the southwestern United States (Yuma and Maricopa, AZ, Uvalde and Edinburgh, TX). Four different water sources were used to gauge if water quality impacted sanitization efficacy. The experimental design was based on an EPA/FDA protocol to assess the efficacy of an antimicrobial product to reduce foodborne bacteria in pre-harvest agricultural water (https://www.fda.gov/media/140640/download) . This protocol dictates that STEC cocktail be added to agricultural water then equilibrated at either temperature (12°C or 32°C); post equilibration, each sanitizer, for each concentration, is injected into the mixed solution. The appointed contact time (1 or 5 minutes) is given and then the solution is neutralized and evaluated. To further growers’ comprehension of best management practices for successful antimicrobial treatment application, the impact of two nitrogen-based fertilizers (UAN32 and CAN17) on the efficacy of Sodium Hypochlorite 6% (chlorine) and PAA against naturally occurring coliforms was also evaluated. The first study provides evidence that chlorine meets EPA’s required 3-log reduction of pathogens in order to receive label approval. At a one-minute contact time, the chlorine treatment resulted in log reduction values (LRVs) ranging from 3.24 to 6.15 regardless of temperature, dose/PPM, or water source. PAA however did not perform as well with LRVs ranged from 0.0 to 1.10 with higher reduction occurring at the higher temperature and dose of PAA. When the contact time of PAA treatment was increased to five minutes, LRVs increased and ranges from 1.5 to 5.4 were observed; the efficacy of the sanitizer increased with increased solution temperature. Furthermore, the addition of nitrogen-based fertilizer to the water source in tandem with treatment application significantly affected the antimicrobial capabilities of chlorine. For chlorine, when applied unaccompanied an average log reduction of 3 logs was seen. However, LRVs decreased on average by 1.34 logs when fertilizer was introduced: with the greatest reduction in efficacy resulting in a nearly 2-log decrease. Contrarily, combined application of PAA and either fertilizer showed little to no interaction with a 0.4 log increase in disinfection efficacy when UAN32 was used. Results indicate that a prolonged contact time may be needed to meet regulations when PAA is used as an antimicrobial treatment. As well, growers must be cautious when applying fertilizer conjointly with antimicrobial treatment to their agricultural waters to ensure compliance with new proposed food safety metrics.
  • Computational Design Optimization and Reliability Assessment of Thermal Systems

    Missoum, Samy; Pidaparthi, Bharath; Li, Peiwen; Chan, Cholik; Ditzler, Gregory (The University of Arizona., 2023)
    Transition to renewable energy solutions, while managing surging energy demand, calls for novel thermal designs. These designs, especially with the advent of additive manufacturing, are becoming increasingly complex and computationally driven. Optimization and reliability analysis of such complex designs typically require several evaluations of the quantities of interest. This is usually accomplished by querying computational models often involving expensive numerical methods like Finite Element Analysis and Computational Fluid Dynamics. To alleviate the computational cost of these design routines, surrogate models can be employed in place of the original model, as they are cheaper to evaluate. In addition, several models, both computational and experimental, are often available to describe a system of interest. These models have varying evaluation costs and fidelities. In general, an expensive high-fidelity model describes the system with the accuracy required for the task at hand, while lower-fidelity models are less accurate but computationally cheaper. In such situations, multi-fidelity procedures can combine information from different levels of fidelity to accelerate the optimization and reliability routines. In this work, these two concepts (i.e., surrogate modeling and multi-fidelity) are employed for optimization and reliability analysis of concentrated solar receiver tubes and heat exchangers.
  • Structural and Diffusion MRI to Study the Effects of Hypertension in Rat Brain Macrostructure and Microstructure

    Trouard, Theodore; Wiskoski, Haley Elizabeth; Hutchinson, Elizabeth; Chen, Nan-kuei (The University of Arizona., 2023)
    Hypertension (HTN) is associated with an increased risk of cardiovascular disease (CVD) and cognitive decline in aging humans, with onset occurring around middle age, and responsible for roughly 7 million deaths worldwide, annually. Prior research has also shown that mid-life HTN is associated with negative effects on brain structure and function in late life. Therefore, it is important to study the symptoms of HTN on the central nervous system as the disease progresses with age, and specifically how this may affect neurological anatomy, development, and function. Animal models are an integral tool in preclinical, translational research of the human body, facilitating greater understanding, treatment, and prevention of diseases such as HTN. The Fischer-344 Cyp1a1-Ren2 transgenic xenobiotic-inducible rat model is an appreciable strain in studies of HTN due to the fact that the induction of increased blood pressure, as induced via the administration of dietary molecule Indole-3-Carbinol (I3C), is reversible, controllable, and dose-dependent in magnitude. The purpose of this study was to investigate the longitudinal effects of induced HTN in macrostructural and microstructural neuroanatomy of F344 Cyp1a1-Ren2 transgenic rats through the use of noninvasive diffusion-weighted MRI (dMRI) and imaging analyses. Results of this study show that even in the face of sustained increases in blood pressure and end-organ damage in the heart and kidney, a majority of the brain remained unaffected in terms of volume and microstructural characteristics. This indicates the presence of an intrinsic, protective mechanism of the brain in this model, forestalling the onset of detrimental effects of HTN on brain structure and function.
  • Estimation of Diffractive Surface Profile using Phase Retrieval Techniques

    Schwiegerling, James T.; Ryu, Jieun; Milster, Thomas D; Sasian, Jose M. (The University of Arizona., 2023)
    An intraocular lens (IOL) is an artificial lens that is inserted into the eye as part of a treatment for cataract or myopia. Among the many types of IOLs, multifocal IOLs with diffractive optics design have been demonstrated to provide superior vision for both distance and near vision after surgery. In this dissertation, methods for estimating the diffractive surface profile of a multifocal diffractive IOL are investigated. Traditionally, several types of instruments have been proposed to determine the phase profile, such as conventional interferometers and Shack-Hartmann sensor. Holography-based setups have also been widely used for surface profile measurement. However, the proposed conventional methods require additional optical components or a reference beam, increasing system complexity and cost of the system. To avoid the limitations of the conventional system stated above, phase retrieval technique is implemented to estimate the diffractive surface profile of an IOL. The phase retrieval technique is the process of recovering the complex-valued function given the magnitude of its Fourier transform. It is natural to investigate the phase of an object, as optical imaging devices only measure the intensity of light and cannot measure the associated phase directly. This dissertation examines several phase retrieval algorithms. The multiplane phase retrieval algorithm described by Gerchberg is implemented for embodiment of the methods. To acquire multiple diffraction patterns, some techniques were used, such as displacing the imaging sensor to record intensities at different planes, modulating phase in Fourier domain using spatial light modulator (SLM) to record a sequence of intensities with different image planes, and modulating phase in pupil plane using SLM to record diffraction patterns at an image plane with phase diversity. Employing the considered phase retrieval schemes, the phase profile of diffractive multifocal intraocular lens was estimated. This dissertation begins with background section, which describes the human eye and basic principles of multifocal intraocular lenses. Chapter 1 covers basic optical structure of the human eye, age-related eye conditions, and fundamentals of intraocular lenses. Chapter 2 motives and describes several basic phase retrieval algorithms and limitations in finding the phase profile of the diffractive surface of an intraocular lens, followed by computational simulations. Chapter 3 deals with phase retrieval technique employed in combination with the multi-plane phase retrieval method, as well as the selected SLM-based phase retrieval technique suitable for finding phase profile of the diffractive intraocular lens. Chapter 4 describes the experimental setup employed for validating the phase retrieval technique to measure the wavefront profile of the intraocular lenses. The calibration procedure implemented in the experiment is discussed. Chapter 5 concludes the dissertation.
  • 3 Essays in Natural Disaster Adaptation

    Bakkensen, Laura; Blair, Logan; Baldwin, Elizabeth; Smith, Craig (The University of Arizona., 2023)
    Natural disasters present a considerable threat to the welfare of society. This is especially true for growing coastal populations facing stronger and more frequent hurricanes. In response, special building codes in the US are now available through the International Code Council (ICC) that mandate technology like reinforced roof-to-wall connections in new coastal homes ex-ante, and ex-post aid programs through the Federal Emergency Management Agency (FEMA) have grown tremendously. However, despite their critical roles, ICC codes have only been fully adopted by one state, Florida, while others debate their costs and market alternatives. There also remain salient concern over the equitable distribution of FEMA aid for which the literature struggles to untangle. This dissertation asks whether hazard-resistant building code institutions hold legitimizing public values outside of the market, in particular the ability to sidestep damage associated with behavioral bias, internalize physical externalities, and attenuate long run recovery for low-income households. In addition, I argue that our current understanding of equity in ex-post aid is limited by geographic and program aggregation, and ask whether FEMA’s Blue Roof Program, which installs temporary roofs following a hurricane, contribute to class and race-based disparities at the program-property level. Employing a novel property-level dataset on damages over time derived from remotely sensed images of Bay County Florida following Hurricane Michael, I find strong evidence that hazard-resistant codes reduced roof damage through improved preparedness, spill over beyond the individual to protect nearby homes from debris, and attenuated long recoveries---especially in low-income areas. This implies that places exposed to similar disaster risk, but resist modern code institutions, may miss welfare gains and a considerable opportunity to protect their most vulnerable populations. Second, I find that FEMA’s Blue Roof Program prioritizes homes with larger roofs, likely due to associated private contract incentives. However, market alternatives appear to perform worse along these dimensions, and neither arrangement discriminated along indicators of class or race such as home value and percent non-white.
  • Managing Uncertainty in Collaborative Governance: Multi-Method Evidence

    Baldwin, Elizabeth; Emerson, Kirk; Ahn, Minwoo; Smith, Craig R.; Jo, Suyeon (The University of Arizona., 2023)
    Scholars have recognized the importance of uncertainty as institutional context in collaborative water management, but the relationship between uncertainty and collaborative performance is mixed. On the one hand, increased uncertainty will positively enhance the performance of collaborative governance through new ties and innovations. On the other hand, certain types of uncertainties are negatively associated with collaborative performance. To understand the puzzle of uncertainty and collaborative performance, I take the problem of groundwater management for theory development and empirical testing. Aquifers are being depleted faster than they can recharge, leaving municipalities, irrigators, and ranchers vulnerable to ever-reducing water availability over time, but the management of groundwater problems is wicked because it involves complex social, ecological, scientific, administrative, and political issues. The effectiveness of collaborative governance depends in large part on the way in which stakeholders perceive, interpret, and use uncertain information.This dissertation fills the theoretical and empirical gap by using multi method research design. The first research question is: What are the nature and characteristics of uncertainty in collaborative governance? This question is addressed based on the in-depth case study of Upper San Pedro Watershed Partnership in Arizona, U.S. Based on the various sources of empirical data, including 22 in-depth interviews, policy reports, and local news articles, conceptual typology and theoretical propositions are proposed to develop theories of collaborative governance under uncertainties. Results suggest that scientific and managerial uncertainty are significant and tend to have negative effects on the performance of groundwater management, but the relationship between uncertainty and collaborative performance can be positively or negatively moderated by the quality of relation management including integrative leadership and cohesion building between participants. Results also suggest that levels and sources of uncertainty tend to change as collaboration evolves and thus the relationship between uncertainty and performance may shift over time. Having recognized that understanding scientific uncertainty is important in groundwater management based on the case study, this dissertation asks two questions: How and to what extent does scientific uncertainty affect collaborative performance? Do collaborative management tools have an impact on different types of collaborative outcomes, particularly under the condition of scientific uncertainty? This dissertation modified a groundwater game experiment where groups of 4-5 participants play a crop choice game for multiple rounds as resource users (Meinzen-Dick et al. 2016). The goal of this game for each participant is to grow as many profitable crops as possible under conditions where all users share groundwater resources with limited ability to recharge. But unlike the original game, where participants had full information about recharge rate, two treatments are introduced about scientific uncertainty in water recharge: uncertainty operationalized as a range of values (Treatment 1) and uncertainty operationalized as competing hydrological models (Treatment 2). Using quantitative and qualitative game experimental data from 30 groups, results suggest that more uncertain information tends to reduce individual earnings and thus increase shared resources. A range of uncertain information has a more significant impact on resource behavior than competing information. Finally, post-experimental analysis shows that diverse collaboration strategies tend to reduce distributional inequity among game participants. This dissertation contributes to the literature of collaborative governance and collective action by explicitly theorizing and modelling the relationship between uncertainty, collaboration process, and performance.
  • Advancing Neural Networks Towards Realistic Settings Using Few-Shot

    Ditzler, Gregory; Hess, Samuel Thomas; Akoglu, Ali; Tandon, Ravi (The University of Arizona., 2022)
    Neural networks have shown remarkable performance across many tasks, including classification, object detection, and image segmentation. Advances in high-performance computing have enabled neural networks to train on extremely large datasets that have resulted in superior performance, often outperforming humans in many tasks. In fact, conventional supervised learning neural networks trained with large volumes of labeled data can produce highly accurate models to classify images, videos, and audio signals. Despite the success of neural networks, their deployment and evaluation are limited to the classes and experiences observed during training. The success of neural networks, however, poses a serious challenge if large labeled datasets are not available to train. Thus, these models are not expected to achieve the same success if there are only a few labeled samples per class. To address this weakness of sample size, an area of research is rapidly evolving known as few-shot learning. Specifically, few-shot learning classifies unlabeled data from novel classes with only one or "a few'' labeled exemplary samples. Unfortunately, few-shot learning comes with its challenges, including reduced classification accuracy with respect to supervised counterparts, requirements on the overall size of the training data, classifier explainability, and evaluation assumptions that can quickly break down with many real-world applications. It is against this background that in this thesis, we present five contributions that expand few-shot performance, explainability, and applicability to new novel tasks. Specifically, our contributions are: (1) A novel few-shot network that improves the classification accuracy over prior models by learning to weight features conditioned on the samples. Conventional techniques perform a one-way comparison of an unlabeled query to a labeled support set; however, the soft weight network allows for two-way cross-comparisons of both query-to-support and support-to-query, which is shown to improve the performance of a few-shot model. (2) A new application and novel few-shot network, namely OrderNet, that can accurately learn an ordering of data given a small labeled dataset. Through pairwise subsampling and episodic training, OrderNet was shown to significantly reduce the amount of training data required to achieve regression accuracy. (3) A new approach for eXplainable Artifical Intelligence (XAI), namely ProtoShotXAI, that uses a few-shot architecture to explain black-box neural networks and is the first approach that is directly applicable to the explanation of few-shot neural networks. (4) A novel similarity metric for a few-shot network that achieves state-of-the-art performance on inductive few-shot tasks. The metric is motivated by the fast approximation of exponentially distributed features in the final layer of a trained few-shot classifier, and maximum log-likelihood estimation. State-of-the-art 1-shot transductive performance is also achieved on imbalanced data using a simple iterative approach with our similarity metric. (5) A novel framework for online detection and classification using few-shot classifiers. In contrast to related work, our lifelong learning framework assumes a continuous data stream of unlabeled and imbalanced data. Additionally, our approach continuously refines classes as new data becomes available while considering computational storage constraints. We demonstrate the capabilities of our proposed approach on benchmark data streams and achieve competitive detection performance and state-of-the-art online classification accuracy.
  • Chiefs, Elections, and Violence: Mobilization and Demobilization of African Voters

    Braithwaite, Jessica M.; Chen, Xiran; Braithwaite, Alex; Cyr, Jennifer; Schuler, Paul; Turnbull, Megan (The University of Arizona., 2023)
    Why do some electoral districts experience more pre-election violence than the others in national elections? This dissertation examines how a particular type of local actor – African chiefs – affects pre-election violence locally. I make two arguments regarding the conditions under which chiefs are capable of deterring pre-election violence targeting their communities, and under which chiefs and their subjects are motivated to participate in pre-election violence. In my dissertation I first argue that centralization of kin groups in precolonial era enhances chiefs’ capability of voter coordination in contemporary time, and in turn reduces the risk of pre-election violence. Using survey- and event-based data from both existing and original datasets, I find a negative relationship between precolonial centralization of kin groups and pre-election violence. Further results of two-stage least squares regressions confirm the internal validity of this relationship. These findings apply to cases where indirect rule was adopted and customary land tenure preserved under colonial government, such as much of the Anglophone West Africa, because precolonial institutions have been better preserved in such cases. My second argument concerns how kin-group-based chieftaincy disputes drive royal families to fight one another during the election periods. Having local aspirants in the challenger families – who seek to change the status quo in chieftaincy disputes – increases the risk that chieftaincy disputes escalate into violent conflicts during the elections. Local aspirants, politicians who have dual identities as political party and royal family members, have particular interests in causing political parties to interfere with chieftaincy disputes. As the outcomes of chieftaincy disputes become associated with the outcomes of national elections, disputing royal families have strong motivation to fight each other during elections. I adopt a most-similar case design based on qualitative data collected through field research in Ghana, and inductively develop a theory of politicization of chieftaincy disputes. The findings of this dissertation demonstrate the complex functions of chiefs and their institutional foundations in African elections. In particular, the institutions of kin group structure local actors’ interests in such a way that they could be motivated to support and undermine democratic processes at the same time. These findings contribute new arguments and evidence to the debate about the relationships between traditional and democratic institutions. In addition, they also highlight the heterogenous colonial legacies between the Anglophone countries in Africa. Precolonial institutions are in general better preserved in Anglophone countries in West Africa then in other countries. Lastly, the findings also have policy implications. Chiefs can become valuable local non-state actors that join forces with international and national actors in pre-election violence prevention. It is also necessary to develop legal and policy instruments that separate politicians from traditional affairs.

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