5.1 Becoming a Volunteer Worker
On March 13, 2022, Shanghai’s number of active COVID cases had risen from one on March 1 to 41, and the number of the asymptomatic cases reached 128—a first in Shanghai since the outbreak of the pandemic two years earlier. On the same day, the Shanghai Municipal Education Commission announced that all universities in Shanghai would transition to so-called lockdown management(封闭管理 feng bi guan li). Shortly after the official announcement, students at X University (anonymized) could still move freely on campus but were no longer allowed to leave it, and faculty and staff were asked to return home before 1pm the same day. The university issued a letter calling for volunteers to remain on campus and support students as they transitioned into lockdown. Many of the faculty and instructors, myself (author 2) included, stepped up as volunteer workers. Most of us thought this volunteer work would involve a couple of days of work, helping students get tested and acquire food, while we stayed overnight in our own office spaces—a task that seemed contained and easy to carry out.
About a week later, on March 27, 2022, the number of asymptomatic COVID cases went up to 4381. At about 8:30 pm that day, the Shanghai government announced a two-phase lockdown of the city, with the eastern Pudong districts locked down from March 28 to April 1, and the Western part of the city (Puxi) locked down from April 1 to 5. Across social media and personal conversations, the atmosphere was nervous, but people mostly remained optimistic that Shanghai’s previously successful COVID management would be re-established. Residents spent the next few days purchasing groceries to last for a week or so and then transitioned into home quarantines, with local neighborhood committees managing daily nucleic acid tests. Yet the numbers of COVID and asymptomatic cases continued to increase dramatically. Through April 5, the daily numbers rose by nearly 20,000 each day. Consequently, the city government of Shanghai did not lift the lockdown after one or two weeks as was originally suggested. In the weeks that followed, many Shanghai residents experienced dramatic shortages of food and other necessities and were confronted with the emotional turmoil of uncertainty over when the lockdown would be lifted.
The often traumatic experiences of city residents in general were mirrored at a smaller scale on our university campus. As the city transitioned into its full lockdown, our volunteer work transformed drastically. Students were no longer allowed to leave their dormitories, and so I became one out of five instructors to manage daily food and water order and delivery as well as daily COVID testing in our department’s building and associated dormitories (a 38-story building, with nearly 1300 student residents). The workload was compounded one day into the official lockdown when a COVID case—the first on campus—was found in our own building.
I still remember vividly the first few days of the lockdown. The four other volunteers and I were asked to wear hazmat suits daily from 6 am to 1 am, a claustrophobic experience that generated its own visceral trauma. Our days were filled with the neverending tasks of delivering food, water, and disinfectant materials to nearly 1300 students dorm by dorm, organizing COVID tests, and dealing with an array of emergencies that we could barely count. We worked without a break, but still struggled to manage students’ basic living needs, often pushing dinner after 9 pm. Because the transition into lockdown occurred so suddenly, we initially lacked basic living supplies for students; drinking water, for example, was limited to 500 milliliters per student for two days. We lobbied for additional volunteer support and resource supplies from the university but did not receive these until a week into the lockdown.
What neither I nor my fellow volunteers could have foreseen at the start of the lockdown was that we would spend the following three months sleeping, living, and working alongside the students in the same building. We would be responsible for not only the procurement of basic necessities such as food and water, and the enabling of mass testing on campus and COVID-case tracking, but also the management of students’ and colleagues’ emotional distress and turmoil. The latter were intensified when it became increasingly clear that the city’s data-driven approach to managing COVID was no longer delivering the“precision” and“people-centered-ness” that it promised. Some of my colleagues joked that far from being an exemplar of Shanghai’s“smart transformation”(智能化 zhi neng hua), the campus was thrown into a much more labor-intensive stage of technology development, akin to the 1990s’ information society. Indeed, the lockdown suddenly made visible to many of us that the promise of a data-driven COVID management approach and zero-COVID itself had been a far cry from reality even prior to the lockdown. The breakdown made visible how both systems had long relied on the volunteer labor of community workers. As a colleague in computer science aptly put it, Shanghai’s data-driven COVID management was花架子 (hua jia zi)—something that sounds good in theory, but lacks substance.
5.2 Data Work: Maintaining the Promise of a Precise Data-Driven System
One of the fundamental promises attached to Shanghai’s data-driven COVID management was its precise, real-time contact tracing and tracking, including the early identification of clusters. The system was promoted as capturing and updating information automatically and in real time, including data on people’s location, test results, and close contacts, down to the smallest grid level. The Shanghai lockdown, however, made it suddenly clear that this level of“precision” had never been achieved automatically, but required extensive situated data work on the ground, with volunteer workers collecting, recording, and making sense of data. When I became a volunteer worker at the university, my colleagues and I discovered that the pre-existing data management had been static and often fragmented. As Jian, my colleague, described it to me:
We have the basic statistical data, but this static data is useless for pandemic prevention. We need real-time population data, which is hard to be collected or self-reported. For example, my mother comes to my home and helps us care for my child. It is very normal. We are unlikely to report this data to the neighborhood committee in real time, although we are required to do that. Very few people do that. I think this phenomenon is very common, not only in our community.
It had become apparent, in other words, how much a data-driven COVID management approach relied upon volunteer workers behind the scenes performing daily data work such as data collection, recording, correction, updating, meshing, and reconciling in order to build and maintain the necessary data infrastructures. This kind of data work was meant to support a bottom-up grid management on our campus. One key task was to build—by hand and as precisely as possible—a data infrastructure for each respective grid (e.g., a campus building, a floor) and then submit the respective data to our university’s upper management. We collected and recorded data such as numbers of students and staff, their demographic information as well as student data (dorm, major, school, head teacher, secretary of the school), dietary needs, data on students’ and staff’s status of testing and test results, and more. We collected and then recorded these data via several digital management systems provided by the university. The Chinese social media and messaging app WeChat constituted the main (and often the only) tool to connect various COVID-related management systems (grid management, student data) into a cohesive whole and supported the communication and collaboration across different units on campus. Our day-to-day work as volunteer workers thus entailed spending hours in WeChat, managing countless WeChat groups.
We used both digital and physical modes of data collection. The technology-mediated collection was meant to crowdsource data, with students having to provide the data we fed into the various systems. A central tool in this process was the WeChat functionality“Group Note” (in Chinese referred to as接龙jie long), which enables participants to relay information in a group chat like a chain or thread of replies. The other commonly used tool was“shared document” to share information broadly. We would set up a new thread, for instance, and each person in the associated WeChat group would relay it to report their own respective situation. Similarly, we created a shared document in Tencent Doc and Kingsoft Doc (two popular Chinese counterparts of Google Doc), and asked people to provide various data such as number of students on each floor, their dietary needs, etc. Functionally, the WeChat“Group Note” was mainly used to collect real-time data (e.g., students’ and staff’s nucleic acid test results and antigen detection test results within 24 hours), while the Shared Docs were mainly used to perform basic statistical analysis (e.g., number of students in each dorm). In addition, we also designed our own technological tools to collect data. For instance, in order to create a localized, campus-specific version of a real-time nucleic acid test situation, we designed a university-specific QR code system to check people’s nucleic acid test results(签到码 qian dao ma). Just as had been the case outside the campus and before the lockdown, students were required to scan this QR code to upload their testing data.
Crucial for this management of students and staff was a process of self-reporting(报数 bao shu) via WeChat. Self-reporting meant, for instance, that students and faculty had to disclose positive test results, a requirement that was contrary to their personal interests and risked centralized quarantine. In order to avoid“inaccurate” self-reporting, we were asked to manually screen each dorm(扫楼 sao lou), calling students one by one. Jiao, a student volunteer, described this process:
I really want to swear at WeChat Group Note/jielong. The jielong number of our grid was always wrong. Because there are a few senior Ph.D. students in our grid, their daily routines were different from ours. They usually get up in the afternoon and miss the morning test and Jielong. So I need to call them to test and Jielong. But it’s so hard to reach them, even harder than reaching the sky.
Issues like these became pervasive, making daily verification of self-reported data one of our main tasks. Put simply, we had to maintain by hand a technological surveillance system that had broken down entirely.
The same was true for mass testing. The university required us to perform mass COVID testing twice a day. One of our tasks as volunteer workers was to ensure that each student in our grid was tested“on time” each day. After students completed a COVID test, they were asked to a) self-report the result in their assigned WeChat groups and b) scan their respective QR codes/qiandaoma on site, then the qiandaoma data would be uploaded to the university’s management system automatically. After all students tied to a particular grid completed their tests, two additional“checks” were performed synchronously (see Figure
2, which shows how test data is flowed from students to frontline management workers and systems within the university grid). First, we compared by hand the results self-reported via WeChat and via the QR code scan, respectively. If the results were inconsistent, we had to search for the assumed missing or incorrect data. We then submitted the final and“correct” number to the school secretary in charge of nucleic acid testing work. Second, the university management system digitally compared test results submitted by students by hand and via the QR scan. If these results were inconsistent, we were required once again to check and correct for inconsistencies.
Verifying consistency across these data sets was part of a continued drive towards a zero-COVID status on campus, even if just in appearance. Inconsistencies, however, arose on a daily basis for various reasons. For instance, a student might have simply forgotten to scan the QR code or scanned the wrong QR code, or the student records in the management system were not updated in time, and so on. One of our key tasks and what occupied us for many hours each day was thus identifying and correcting errors in an effort to re-establish the status and image of a COVID-free campus.
The normative coding of these requirements and the affective labor entailed in trying to meet them was brought home to me one day when we received a note from the campus hospital that, at the end of the day, there had been 6 inconsistent recordings in the system. We were asked by hospital staff and administrators to identify the“culprits,” a term that not so subtly implied that such inconsistencies could only be attributed to students or staff breaking the campus rules of lockdown, or to our own faulty data collection. After hours of checking across spreadsheets and WeChat groups, we discovered that the 6“inconsistencies” were due to 6 students who lived and worked on a different campus altogether, but had mistakenly scanned the wrong (our campus’) QR code.
These various incidents of breakdown made visible the extent to which human labor had always been necessary to maintain Shanghai’s data-driven COVID management. Even people well-acquainted with the technologies and systems involved found themselves reassessing their understandings. Qin, the project manager of Shanghai’s data-driven epidemiological investigation system, explained how the lockdown changed her own perception of Shanghai’s data-driven governance:“There is no so-called data-drivenness,” she said,
[…it] is fully human-driven, it is very traditional. There is human labor which we refer to as liudiaoyuan(流调员 liu diao yuan). [It means] when a positive COVID case is identified, liudiaoyuan needs to trace the [person’s] recent 14-day travel histories and close contacts, and then notifies these close contacts. This data is added into the data-driven management system by the liudiaoyuan as well, manually. I was very shocked when I learned about this process in the beginning. I couldn’t believe this was Shanghai, an international metropolis.
Yet despite such reassessments and first-hand experience with the extreme overwork and exhaustion brought about by maintaining and managing the data systems, many of my fellow volunteer workers told me that they still considered a data-driven COVID management approach to be the future and the appropriate direction for Shanghai’s government to take in the long run, even after the pandemic. As Bin, a senior leader in a state-owned technology enterprise who worked closely with the Computer Science department on our campus, and a community frontline volunteer during the lockdown, told us,“We now know that complex, advanced systems, with this or that function, are useless. Data—real-time, accurate, safe and connected data, is the way to go.” Indeed, one might argue that most salient accomplishment of these data workers was thus the maintenance of belief in a data-driven future. They maintained, in other words, the promise of a precise data-driven city management system, for Shanghai and beyond, despite the breakdowns and a widening societal critique of the government’s approach to COVID management.
5.3 Articulation Work via Data Work: Maintaining the Promise of a Well-Managed Campus
On March 27, 2022, after the Shanghai government announced the city’s two-phase lockdown, the university’s“pandemic prevention working group” began building a digital grid architecture and mapped the university’s original organizational structure into a grid management hierarchy (see Figure
3). Faculty, staff and students were assigned to a specific grid. At the same time, the university’s original organizational hierarchy (schools, departments) remained in place. Each student was managed under both their original organizational hierarchy (e.g., school and department) and a grid management hierarchy (e.g., segments and buildings). A key task of our volunteer labor, beyond managing the data systems mentioned above, also became the articulation and communication between these two hierarchies (referred to as tiao-kuai in Chinese) in order to manage the lockdown. And the articulation and communication work were largely shaped and centered around data work and data systems.
The facilitation and coordination between different structures of population management was one of the biggest challenges of China’s pandemic management system writ large. On the city-level, the interaction between different hierarchies of social management was performed by mid-level government officials. A vast workforce of volunteers labored for these officials to collect, record, and submit data as well as identify and report problems. At the university-level, volunteer workers were responsible not only for the frontline management of their respective grids (e.g., a building), but also for the collection and provision of data on behalf of the larger grid management system at the university-level. This work included frequent communication between the organizational structures of tiao and kuai to further facilitate information flow across all levels of management.
Since the volunteer workers assigned to physical segments (e.g., a building) had the most comprehensive view of the actual situation, they took on the role of“articulators” on behalf of various grid units of both tiao and kuai hierarchies. This work entailed streamlining, via phone calls and WeChat messages, between the different management structures. And it entailed making sense of various data on campus residents. Without a commonly shared platform for storage and access, this data was highly fragmented, dispersed across different places and platforms, e.g., the university’s digital COVID platform and the school’s COVID platform, with data held across different parties, e.g., class teachers, monitors, etc. Jin, the manager of our particular grid questioned the efficacy of this process:
I feel the biggest problem is how to connect different data parts. Currently, we repeatedly seek, collect, record, and exchange data every day. Sometimes, different level management units need the same data, and then we provide these data to different people over and over. I know this is evitable, but it is really a huge waste of resources and time. This concerned me very deeply. Why couldn’t we put this data in a public place or a public platform? Or, either top-down or down-top, instead of the current mixed ways.
While different stakeholders required different data to reach their own goals, volunteer workers took it upon themselves to identify and connect data. Every day, for instance, we collected and updated students’ dietary data (the number of normal boxed meals, halal meals, or vegetarian meals, and the number of breakfast, lunch and dinner, etc.) and transferred them to the dining hall; and recorded students’ and staff’s nucleic acid test and antigen detection data and transferred them to the campus hospital. Volunteers increasingly understood that their job, perhaps most importantly, was to maintain the veneer of a data-driven management system, with much data gathering and collecting revolving around correcting system errors, and recording the same data over and over and across several platforms to satisfy the university mid-level management’s aspirations to maintain a data-driven and dynamic approach to zero-COVID.
To create some form of meaning in this rather pointless process, our team began working on a user-friendly interface to inform students of important announcements and help them deal with information and data overload. We streamlined various data points into one aggregated whole (see Figure
4-a), which was released daily at 9 pm, making data not only available to the students, but also offering high-level interpretation and meaning-making. The volunteer workers paired dull, logistical information with fun and playful emojis, memes, and stickers, providing a sense of warmth and“positive feeling” (see Figure
4-b and 4-c). The undergraduate student who designed the pictured meal tickets explained their motivation:
Us students have been eating box lunches for a long time. Many students were very Negative about that and complained a lot. I can’t change this situation, but I hope to do something to make it better. So I designed these meal tickets. I hope to bring a little bit of ease and smile to them, and make the hard time a little better.
In a similar vein, several faculty and students with technological expertise volunteered their skills to aid the campus-wide COVID management and create feelings of stability and order. For instance, Jian, a professor of computer science, designed a Microsoft Office-based automatic statistical tool that captured and automatically uploaded information students submitted via the WeChat groups (mentioned in 5.1). He talked with great pride about designing the system:
In the beginning, I found that the work I needed to do was too messy. Most of my time was spent on repetitive data work, counting numbers from Group Note in different WeChat groups, catching up with WeChat messages, inputting data into excel, checking the consistency, etc. This work was very cumbersome, time-consuming, and made me dizzy. So I designed this tool. It simplified the process and automated the repetitive work. My tool helps improve the efficiency tremendously while also avoiding many manually-caused errors.
This simple but useful tool quickly became popular across campus and was adopted by other volunteers. Following Jian’s footsteps, other students and faculty developed a variety of technological workarounds to help frontline management. Min, the dean of the School of Computer Science, who had become the manager of the volunteer group who took care of food delivery, wrote a distribution and planning algorithm. He explained that the goal was“how to make the most of limited resources, use minimal cost and the fastest way to deliver food to students, and let them eat the hot meal.” His algorithm significantly improved food delivery efficiency and reduced errors. Solving such problems for students and frontline management, he told me, gave him a sense of achievement and made him feel useful. One day, after finishing the food delivering task, he posted the following message in the WeChat group comprised of volunteer workers:
Today our group fully optimized the path and only used half an hour to finish the delivering of 1132 lunches for 11 buildings. We do whatever we can do(“有一点光,就发一点热” you yi dian guang, jiu fa yi dian re), but don’t do it recklessly. We emphasized efficiency and optimization. This is the spirit of our CSers.
The more literal meaning of the Chinese phrase“有一点光,就发一点热” mentioned here is significant in that it reveals a broader sentiment of providing a feeling of warmth and care during an increasingly brutal experience:“as long as we have some light (just like candles), we shall continue spreading heat/warmth.” While technological inventions like Jian’s were simple, relying on basic tools such as Microsoft Excel and shared documents, they sought to enable positive feelings about the situation. Jian and others told us that their technological intervention was not about the advancement of data, but about cultivating an attitude of problem-solving, i.e., the feeling that broken systems can and should be fixed by citizens, or in Jian’s words:
I don’t think what I have designed is a big contribution. These tools were mainly for ourselves, and for reducing our own workload. We just want to take care of the university and our students, and don’t cause trouble to the government(不给政府添麻烦 bu gei zheng fu tian ma fan).
Most volunteer workers, including myself, did not not identify with the state discourse of performing a heroic task or historic mission. The work was much more mundane. It involved making do and providing a sense of relief for the students, or as Min put it, fostering a sense that we could“do something during this difficult time.”
5.4 Emotional Work: Managing Feelings and Maintaining Positive Energy
As volunteer workers became more experienced and technologically adept at supporting management problems, more thorny issues arose. In the first half of the lockdown in April, Shanghai experienced a dramatic shortage of food and other daily necessities. The supplies our university obtained often failed to meet the needs of students and the government’s promise to deliver“one item per person.” Thus the challenge of how to distribute limited supplies in a fair manner to students as well as managing their worries and frustrations over the lack of supplies became a central task for volunteer workers. The emotional intensity engendered by this food scarcity was made starkly clear when a group of Masters’ students fought in one of the university’s WeChat groups for several days over the distribution of a single bag of chips. Scenes like these abounded, with volunteer workers attempting to bridge distressed students and an increasingly desperate university management.
Further into the lockdown, many of us felt acutely that our patience was stretched thin, and that we had reached our limits both mentally and emotionally—a sentiment that was also expressed by city residents in general. Stories about suicides and various traumatic experiences of people put in centralized quarantine made their rounds on social media (but were often quickly censored). Uncertainty over when the lockdown would end made the situation feel unbearable, with many people commenting that the city had broken down not just infrastructurally, but in terms of the rational and scientific approach to management that it had claimed to have mastered. Volunteer workers were deeply exhausted from the laborious management and emotional work, with students struggling as they were confined to their rooms, frustrated, panicked, and anxious. Bo, the leader of one segment and one of the senior managers at the university, offered advice from his own experience trying to regain students’ trust:
Many students sent messages to me, especially the students of our School (of Engineering). They know“he is here” and might feel a bit more reassured. Some feelings like "we are not alone" or "our leadership is also staying with us and could help them anytime" are important. Some of the students might feel it is useless to share their issues to their faculties, because faculties are less resourceful or powerful, so they talk to me. It is a kind of trust. I must be responsive and present for them, no matter how busy I am.
Jin, one of the volunteers, also tried to add a personal touch to the text messages that students were bombarded with, and that they described experiencing as“cold” and“emotionless” orders:
I never liked using texts to notify students before. Currently, we have no choice, WeChat seems like the most convenient way. But when I send a text notification through WeChat, I always follow up with a kind voice message. The text messages were really void of any emotions. If we don’t follow up with our own voices and provide a sense of affection for the students, it will sound very cold for students.
At the same time, a group of student volunteers established a public shared document that encouraged students to share their personal feelings and difficulties, ranging from living conditions to mental well-being. The document also offered mutual support. The tool was quickly disseminated around campus. We saw requests for help and testimonials of shared difficulties flood in, as did the offerings of peer support. Faculty volunteer workers also turned to the mutual care tool to provide support and backup. The tool eventually drew the attention of the upper management at the university and received further support to improve its UI and accessibility. Apparently, university leadership, like the volunteers themselves, had come to recognize that a central task of the latter was not only maintaining and supplementing technological work, but performing the affective work of maintaining the mental health and well-being of the on-campus community, creating feelings of“positive energy”(正能量
zheng neng liang) [
9,
53] about a far from positive situation.