Social platforms are often used by communities to spread awareness and advocate for change; such platforms are rarely designed for participation in institutional processes. We call this the process-platform gap: institutional processes require structured, sustained forms of participation that social platforms are not designed to support. How might social platforms evolve to support greater participation in institutional processes? We study this question via a case study of the scientific drug development and regulatory process and how it is influenced by contributions from the Amyotrophic Lateral Sclerosis (ALS) patient community. The ALS community intervenes at multiple stages of the research process with flexible, novel use of current social platforms. Our work focuses on three ways the ALS community uses social platforms to expedite drug development. First, the community directly used general-purpose features--like hashtags and tagging on X--to raise funds through viral campaigns like the Ice Bucket Challenge. Second, patients repurposed self-tracking features--like functional assessment scores on PatientsLikeMe--to run studies for novel drugs. Third, the community uses specialized platforms--like regulations.gov--for focused formal work by submitting public comments that critique and shape the Food and Drug Administration's (FDA) drug development guidelines. One limitation of the community's use of social platforms is the mismatch between social platform designs and institutional workflows, leading to a persistent process-platform gap. This limits the potential for sustained dialogue, collaboration, and significant integration of community-led efforts into institutional decision-making. Our work provides design recommendations to reduce the process-platform gap.

Institutional decisions deeply afect people's lives.Many communities seek to infuence policy and science by advocating for themselves.Self-advocacy is especially important for people with disabilities who may have diferent needs or ways of interacting with the world.While formal advocacy groups such as nonproft organizations or government agencies provide structured support, people increasingly advocate independently through digital platforms in ways that might ofer more accessible participation.One community that uses digital platforms for advocacy is the ALS community.Amyotrophic Lateral Sclerosis (ALS) is a fatal neurodegenerative disorder that causes motor disability, with no cure and limited treatment options.Our research examines the advocacy strategies of the ALS community on two platforms: a public comment system (regulations.gov),and a social platform (X).Our work compares how people with ALS advocate across diferent platforms for policies and institutional processes that better accommodate their lived experience.Our results suggest that digital platforms can complement traditional advocacy by enabling faster, more direct participation for anyone with internet access.By connecting themes from ASSETS and CSCW scholarship, we suggest ways to design more inclusive civic technologies that better support disabled communities in infuencing policymaking.

People have scientific questions and folk theories; yet most lack the expertise to investigate them. How might people transform their questions into experiments that inform both science and their lives? This paper demonstrates how online volunteers can collaboratively design and run experiments using a novel social computing system. The Galileo system provides procedural support using three techniques: 1) experimental design workflow that provides just-in-time training; 2) review workflow with scaffolded questions; and 3) automated routines for data collection. We present two empirical investigations: a study and a field deployment with online volunteers across 16 and 8 countries respectively. People generated structurally-sound experiments on personally meaningful topics; three communities ran a week-long experiment each. We identify two key challenges for citizen-led experimentation—supporting different expertise levels and providing recruitment guidance—and provide specific suggestions from the social computing literature. Our results highlight the promise and challenges of citizen-led knowledge work like experimentation.

Misunderstandings of science affect many lives. Novices commonly misunderstand explanations by overly relying on surface details instead of evaluating underlying logic. Prior work has found adding a patina of neuroscience leads readers towards positively assessing explanations. How might we help people better understand science explanations? A between-subjects experiment tested whether asking readers to reconstruct experiments leads them to focus more on underlying logic. Participants relied less on irrelevant surface details when reconstructing experiments. However, this did not impact their subsequent assessment of explanations. Our results suggest that reconstruction is a useful strategy for understanding explanations but is not readily transferred towards evaluating explanations.

People's lived experiences provide intuitions about health. Can they transform these personal intuitions into testable hypotheses that could inform both science and their lives? This paper introduces an online learning architecture and provides system principles for people to brainstorm causal scientific theories. We describe the Learn-Train-Ask workflow that guides participants through learning domain-specific content, process training to frame their intuitions as hypotheses, and collaborating with anonymous peers to brainstorm related questions. 344 voluntary online participants from 27 countries created 399 personally-relevant questions about the human microbiome over 4 months, 75 (19%) of which microbiome experts found potentially scientifically novel. Participants with access to process training generated hypotheses of better quality. Access to learning materials improved the questions' microbiome-specific knowledge. These results highlight the promise of performing personally-meaningful scientific work using massive online learning systems.

Learners worldwide collectively spend millions of hours per week testing their skills on assignments with known answers. Might some of this time fruitfully be spent posing and exploring novel questions? This paper investigates an approach for learners to contribute scientific ideas. The Gut Instinct system embodies this approach, hosting online learning materials and invites learners to collaboratively brainstorm potential influences on people's microbiome. A between-subjects experiment compared the performance of participants who engaged in just learning, just contributing, or a combination. Participants in the learning condition scored highest on a summative test. Participants in both the contribution and combined conditions generated novel, useful questions; there was not a significant difference between the two. Though participants in the combined condition both learned and contributed, this setting did not exhibit an additive benefit, such as better learning in the combined condition. These results highlight the promise and difficulty of double-bottom-line learning experiences.

Verifying the integrity of outsourced data is a classic, well-studied problem. However current techniques have fundamental performance and concurrency limitations for update-heavy workloads. In this paper, we investigate the potential advantages of deferred and batched verification rather than the per-operation verification used in prior work. We present Concerto, a comprehensive key-value store designed around this idea. Using Concerto, we argue that deferred verification preserves the utility of online verification and improves concurrency resulting in orders-of-magnitude performance improvement. On standard benchmarks, the performance of Concerto is within a factor of two when compared to state-of-the-art key-value stores without integrity.

Peer assessment is rapidly growing in online learning, as it presents a method to address scalability challenges. However, research suggests that the benefits of peer review are obtained inconsistently. This paper explores why, introducing three ways that framing task goals significantly changes reviews. Three experiments manipulated features in the review environment. First, adding a numeric scale to open text reviews was found to elicit more explanatory, but lower quality reviews. Second, structuring a review task into short, chunked stages elicited more diverse feedback. Finally, showing reviewers a draft along with finished work elicited reviews that focused more on the work's goals than aesthetic details. These findings demonstrate the importance of carefully structuring online learning environments to ensure high quality peer reviews.

Student discussions over video in massive classes allow students to explore course content, share personal experiences and get feedback on their ideas. However, such discussions frequently turn into casual conversations without focusing on the curriculum and the learning objectives. This short paper explores whether students can achieve multiple learning objectives by solving challenges collaboratively during discussions. We introduce `think-pair-share' technique for video discussions. Our pilot results, drawn from a Coursera class, suggest that participants prefer to exchange information with their peers using personal stories and connecting stories with curriculum increases participant engagement.

We consider the problem of representing numbers in close to optimal space and supporting increment, decrement, addition and subtraction operations efficiently. We study the problem in the bit probe model and analyse the number of bits read and written to perform the operations, both in the worst-case and in the average-case. A counter is space-optimal if it represents any number in the range [0,...,2 n − 1] using exactly n bits. We provide a space-optimal counter which supports increment and decrement operations by reading at most n − 1 bits and writing at most 3 bits in the worst-case. To the best of our knowledge, this is the first such representation which supports these operations by always reading strictly less than n bits. For redundant counters where we only need to represent numbers in the range [0,...,L] for some integer L < 2 n − 1 using n bits, we define the efficiency of the counter as the ratio between L + 1 and 2 n . We present various representations that achieve different trade-offs between the read and write complexities and the efficiency. We also give another representation of integers that uses n + O(logn ) bits to represent integers in the range [0,...,2 n − 1] that supports efficient addition and subtraction operations, improving the space complexity of an earlier representation by Munro and Rahman [Algorithmica, 2010].