Department of Informatics

This issue of the California Journal of Politics and Policy is produced in collaboration with the Kem C. Gardner Policy Institute at the David Eccles School of Business at the University of Utah.Drawing on the expertise of political scientists, economists, and practitioners from 13 west-ern states, the reports summarize each state’s budget for the 2017‒2018 fiscal year. These reports delve into how the states’ financial well-being affected legislation and just as importantly how legislation affected the states’ financial well-being.While most states seem to be financially sound, if not thriving, each report highlights possi-ble threats in the coming years, whether they be political, economic, or natural concerns. One theme across this year’s budget reports is how the 2016 election of President Donald Trump has affected legislation and fiscal health of the states. A second theme in the budget papers is the need to plan for the next recession.

UNLABELLED: Concerns regarding the potential negative impacts of digital technology use on youth mental health and well-being are high. However, most studies have several methodological limitations: relying on cross-sectional designs and retrospective reports, assessing technology use as an omnibus construct, and focusing on between- instead of within-person comparisons. This study addresses these limitations by prospectively following young adolescents (n = 388) over a 14-day ecological momentary assessment study to test whether adolescents digital technology use is linked with self-reported emotional dysregulation and self-esteem and whether these relationships are stronger for adolescent girls than boys. We found no evidence that adolescents experienced higher emotional dysregulation (b = - .02; p = .07) and lower self-esteem (b = .004; p = .32) than they normally do on days where they use more technology than they normally do (within-person). Adolescents with higher average daily technology use over the study period did not experience lower levels of self-esteem (between-person, b = - .02; p = .13). Adolescents with higher average daily technology use across the two-week period did report higher levels of emotional dysregulation (p = .01), albeit the between-person relation was small (b = .08). There was no evidence that gender moderated the associations, both between and within adolescents (bs = - .02-.13, p = .06 - .55). Our findings contribute to the growing counter-narrative that technology use does not have as large of an impact on adolescents mental health and well-being as the public is concerned about. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s42761-024-00282-w.

The recent proliferation of large language models (LLMs) has led to divergent narratives about their environmental impacts. Some studies highlight the substantial carbon footprint of training and using LLMs, while others argue that LLMs can lead to more sustainable alternatives to current practices. We reconcile these narratives by presenting a comparative assessment of the environmental impact of LLMs vs. human labor, examining their relative efficiency across energy consumption, carbon emissions, water usage, and cost. Our findings reveal that, while LLMs have substantial environmental impacts, their relative impacts can be dramatically lower than human labor in the U.S. for the same output, with human-to-LLM ratios ranging from 40 to 150 for a typical LLM (Llama-3-70B) and from 1200 to 4400 for a lightweight LLM (Gemma-2B-it). While the human-to-LLM ratios are smaller with regard to human labor in India, these ratios are still between 3.4 and 16 for a typical LLM and between 130 and 1100 for a lightweight LLM. Despite the potential benefit of switching from humans to LLMs, economic factors may cause widespread adoption to lead to a new combination of human and LLM-driven work, rather than a simple substitution. Moreover, the growing size of LLMs may substantially increase their energy consumption and lower the human-to-LLM ratios, highlighting the need for further research to ensure the sustainability and efficiency of LLMs.

The Proof-of-Work algorithm that underlies Bitcoin, Ethereum w , 1 and many other cryptocurrencies is well known for its energy-intensive requirements. The Proof-of-Stake algorithm that underlies Ethereum and various other cryptocurrencies is less impactful environmentally, but it has a second, looming issue: the problem of wealth inequality. We have developed an alternative to Proof-of-Work and Proof-of-Stake, called Proof-by-Location, that has the potential to address both of these issues. This article describes Proof-by-Location and a financial platform called Xylem that is based on it. This platform seeks to distribute transaction fees to billions of cryptocurrency “Notaries” around the world (essentially, anyone with a smartphone), who work together to establish a distributed consensus about financial transactions. In this article, we demonstrate that this platform can scale to more than 3.9 trillion transactions per year (more than triple the number of digital payments per year currently occurring). We show a reduction of electricity usage per transaction of 99.9999914% compared to Bitcoin, 99.999905% compared to Ethereum w , 99.83% compared to Ethereum, and 95.9% compared to the Visa financial services company. We demonstrate that this platform would have a redistributive rather than consolidatory effect on wealth compared to any of these platforms, leading to a source of income for more than 1 billion people around the world, including more than 110 million in the bottom 10th to 20th percentile by income, with income for that group equivalent to 8.8 million full-time jobs. Finally, this currency provides a positive, non-compulsory mechanism for shaping human habitation patterns in ways that can slow global biodiversity loss and enable ecological restoration. Using Xylem as a global financial infrastructure could lead to significantly better social and environmental outcomes than existing financial platforms. 2

Cash transfer policies have been widely discussed as mechanisms to curb intergenerational transmission of socioeconomic disadvantage. In this paper, we take advantage of a large casino-funded family transfer program introduced in a Southeastern American Indian Tribe to generate difference-in-difference estimates of the link between childrens cash transfer exposure and third grade math and reading test scores of their offspring. Here we show greater math (0.25 standard deviation [SD], p =.0148, 95% Confidence Interval [CI]: 0.05, 0.45) and reading (0.28 SD, p = .0066, 95% CI: 0.08, 0.49) scores among American Indian students whose mother was exposed ten years longer than other American Indian students to the cash transfer during her childhood (or relative to the non-American Indian student referent group). Exploratory analyses find that a mothers decision to pursue higher education and delay fertility appears to explain some, but not all, of the relation between cash transfers and childrens test scores. In this rural population, large cash transfers have the potential to reduce intergenerational cycles of poverty-related educational outcomes.

Large language models (LLMs) hold great promise in summarizing medical evidence. Most recent studies focus on the application of proprietary LLMs. Using proprietary LLMs introduces multiple risk factors, including a lack of transparency and vendor dependency. While open-source LLMs allow better transparency and customization, their performance falls short compared to the proprietary ones. In this study, we investigated to what extent fine-tuning open-source LLMs can further improve their performance. Utilizing a benchmark dataset, MedReview, consisting of 8161 pairs of systematic reviews and summaries, we fine-tuned three broadly-used, open-sourced LLMs, namely PRIMERA, LongT5, and Llama-2. Overall, the performance of open-source models was all improved after fine-tuning. The performance of fine-tuned LongT5 is close to GPT-3.5 with zero-shot settings. Furthermore, smaller fine-tuned models sometimes even demonstrated superior performance compared to larger zero-shot models. The above trends of improvement were manifested in both a human evaluation and a larger-scale GPT4-simulated evaluation.

BACKGROUND: It is difficult for families to navigate and access services for their children with autism. Barriers to service access are compounded among families from low-resourced backgrounds. OBJECTIVE: The purpose of our study was to explore the development of an app to facilitate access to services among families of children with autism from low-resourced backgrounds. Our specific aims were to explore feedback from an advisory board about the app and to explore feedback from navigators about the app. METHODS: Via a multistage codevelopment process, we elicited feedback from 5 key parties: the research team, a community organization, the app development team, the advisory board, and family navigators. Collectively, 36 individuals provided feedback about the development of the app via individual interviews, focus groups, observations, and surveys. The key features of the app included a dashboard showing the service needs of the family and related resources, a messaging feature between the family, the navigator, and the supervisor, and a fidelity checklist and evaluation feature. RESULTS: The advisory board provided feedback about the app to increase its user-friendliness, include the ability to develop an action plan, improve the identification of needed services, and add information about service providers. Navigators suggested that the app should connect navigators to one another, have a clearer purpose for the notes section, and reflect an easier log-in process. Navigators also wanted training to role-play using the app. After participating in a role play using the app, navigators reported significantly more satisfaction with the app and greater usefulness (P<.001). CONCLUSIONS: Our work sheds light on the importance of eliciting feedback from end users, especially users who are often overlooked by the research community and app developers. Further, it is important to elicit feedback in multiple ways to improve the app.

Recent studies indicate that Generative Pre-trained Transformer 4 with Vision (GPT-4V) outperforms human physicians in medical challenge tasks. However, these evaluations primarily focused on the accuracy of multi-choice questions alone. Our study extends the current scope by conducting a comprehensive analysis of GPT-4Vs rationales of image comprehension, recall of medical knowledge, and step-by-step multimodal reasoning when solving New England Journal of Medicine (NEJM) Image Challenges-an imaging quiz designed to test the knowledge and diagnostic capabilities of medical professionals. Evaluation results confirmed that GPT-4V performs comparatively to human physicians regarding multi-choice accuracy (81.6% vs. 77.8%). GPT-4V also performs well in cases where physicians incorrectly answer, with over 78% accuracy. However, we discovered that GPT-4V frequently presents flawed rationales in cases where it makes the correct final choices (35.5%), most prominent in image comprehension (27.2%). Regardless of GPT-4Vs high accuracy in multi-choice questions, our findings emphasize the necessity for further in-depth evaluations of its rationales before integrating such multimodal AI models into clinical workflows.

This article provides three major contributions to the literature: we provide granular information on the development of student argumentative writing across secondary school; we replicate the MacArthur et al. model of Natural Language Processing (NLP) writing features that predict quality with a younger group of students; and we are able to examine the differences for students across language status. In our study, we sought to find the average levels of text length, cohesion, connectives, syntactic complexity, and word-level complexity in this sample across Grades 7-12 by sex, by English learner status, and for essays scoring above and below the median holistic score. Mean levels of variables by grade suggest a developmental progression with respect to text length, with the text length increasing with grade level, but the other variables in the model were fairly stable. Sex did not seem to affect the model in meaningful ways beyond the increased fluency of women writers. We saw text length and word level differences between initially designated and redesignated bilingual students compared to their English-only peers. Finally, we see that the model works better with our higher scoring essays and is less effective explaining the lower scoring essays.