The Research Symbionts Awards are given annually to recognize “symbiosis” in the form of data sharing. They are a companion award to the Research Parasite Awards which recognize superb examples of secondary data reuse. The award includes money to travel to the Pacific Symposium Computing (unfortunately, Mako wasn’t able to take advantage of this!) as well the plush fish with parasitic lamprey shown here.
In addition to the award given to Mako, Dr. Leonardo Collado-Torres was announced as the recipient of the health-specific Early Career Symobiont award for his work on Recount2.
I’ve heard a surprising “fact” repeated in the CHI and CSCW communities that receiving a best paper award at a conference is uncorrelated with future citations.
Although it’s surprising and counterintuitive, it’s a nice thing to
think about when you don’t get an award and its a nice thing to say to
others when you do. I’ve thought it and said it myself.
It also seems to be untrue. When I tried to check the “fact”
recently, I found a body of evidence that suggests that computing papers
that receive best paper awards are, in fact, cited more often than
papers that do not.
The source of the original “fact” seems to be a CHI 2009 study by Christoph Bartneck and Jun Hu titled “Scientometric Analysis of the CHI Proceedings.”
Among many other things, the paper presents a null result for a test of
a difference in the distribution of citations across best papers
awardees, nominees, and a random sample of non-nominees.
Although the award analysis is only a small part of Bartneck and Hu’s
paper, there have been at least two papers have have subsequently
brought more attention, more data, and more sophisticated analyses to
the question. In 2015, the question was asked by Jaques Wainer, Michael
Eckmann, and Anderson Rocha in their paper “Peer-Selected ‘Best Papers’—Are They Really That ‘Good’?“
Wainer et al. build two datasets: one of papers from 12 computer
science conferences with citation data from Scopus and another papers
from 17 different conferences with citation data from Google Scholar.
Because of parametric concerns, Wainer et al. used a non-parametric
rank-based technique to compare awardees to non-awardees. Wainer et al.
summarize their results as follows:
The probability that a best paper
will receive more citations than a non best paper is 0.72 (95% CI =
0.66, 0.77) for the Scopus data, and 0.78 (95% CI = 0.74, 0.81) for the
Scholar data. There are no significant changes in the probabilities for
different years. Also, 51% of the best papers are among the top 10% most
cited papers in each conference/year, and 64% of them are among the top
20% most cited.
Lee looked at 43,000 papers from 81 conferences and built a
regression model to predict citations. Taking into an account a number
of controls not considered in previous analyses, Lee finds that the
marginal effect of receiving a best paper award on citations is
positive, well-estimated, and large.
Why did Bartneck and Hu come to such a different conclusions than later work?
My first thought was that perhaps CHI is different than the rest of
computing. However, when I looked at the data from Bartneck and Hu’s
2009 study—conveniently included as a figure in their original study—you
can see that they did find a higher mean among the award
recipients compared to both nominees and non-nominees. The entire
distribution of citations among award winners appears to be pushed
upwards. Although Bartneck and Hu found an effect, they did not find a statistically significant effect.
Given the more recent work by Wainer et al. and Lee, I’d be willing
to venture that the original null finding was a function of the fact
that citations is a very noisy measure—especially over a 2-5
post-publication period—and that the Bartneck and Hu dataset was small
with only 12 awardees out of 152 papers total. This might have caused
problems because the statistical test the authors used was an omnibus
test for differences in a three-group sample that was imbalanced heavily
toward the two groups (nominees and non-nominees) in which their
appears to be little difference. My bet is that the paper’s conclusions
on awards is simply an example of how a null effect is not evidence of a
non-effect—especially in an underpowered dataset.
Of course, none of this means that award winning papers are better.
Despite Wainer et al.’s claim that they are showing that award winning
papers are “good,” none of the analyses presented can disentangle the
signalling value of an award from differences in underlying paper
quality. The packed rooms one routinely finds at best paper sessions at
conferences suggest that at least some additional citations received by
award winners might be caused by extra exposure caused by the awards
themselves. In the future, perhaps people can say something along these
lines instead of repeating the “fact” of the non-relationship.
It’s Ph.D. application season and the Community Data Science Collective is recruiting! As always, we are looking for talented people to join our research group. Applying to one of the Ph.D. programs that Aaron, Mako, and Sayamindu are affiliated with is a great way to do that.
This post provides a very brief run-down on the CDSC, the different universities and Ph.D. programs we’re affiliated with, and what we’re looking for when we review Ph.D. applications. It’s quite close to the deadline for some of our programs, but we hope this post will still be useful to prospective applicants now and in the future.
What are these different Ph.D. programs? Why would I choose one over the other?
The group currently includes three faculty principal investigators (PIs): Aaron Shaw (Northwestern University), Benjamin Mako Hill (University of Washington in Seattle), and Sayamindu Dasgupta (University of North Carolina at Chapel Hill). The three PIs advise Ph.D. students in multiple Ph.D. programs at their respective universities. Our programs are each described below.
Although we often work together on research and serve as co-advisors to students in each others’ projects, each faculty person has specific areas of expertise and unique interests. The reasons you might choose to apply to one Ph.D. program or to work with a specific faculty member include factors like your previous training, career goals, and the alignment of your specific research interests with our respective skills.
At the same time, a great thing about the CDSC is that we all collaborate and regularly co-advise students across our respective campuses, so the choice to apply to or attend one program does not prevent you from accessing the expertise of our whole group. But please keep in mind that our different Ph.D. programs have different application deadlines, requirements, and procedures!
Benjamin Mako Hill is an Assistant Professor of Communication at the University of Washington. He is also an Adjunct Assistant Professor at UW’s Department of Human-Centered Design and Engineering (HCDE). Although almost all of Mako’s students are in the Department of Communication, he also advises students in the Department of Computer Science and Engineering and can advise students in HCDE as well—although he typically has no ability to admit students into those programs. Mako’s research focuses on population-level studies of peer production projects, computational social science, and efforts to democratize data science.
There’s no easy or singular answer to this. In general, we look for curious, intelligent people driven to develop original research projects that advance scientific and practical understanding of topics that intersect with any of our collective research interests.
To get an idea of the interests and experiences present in the group, read our respective bios and CVs (follow the links above to our personal websites). Specific skills that we and our students tend to use on a regular basis include experience consuming and producing social science and/or social computing (human-computer interaction) research; applied statistics and statistical computing, various empirical research methods, social theory and cultural studies, and more.
Formal qualifications that speak to similar skills and show up in your resume, transcripts, or work history are great, but we are much more interested in your capacity to learn, think, write, analyze, and/or code effectively than in your credentials, test scores, grades, or previous affiliations. It’s graduate school and we do not expect you to show up pre-certified in all the ways or knowing how to do all the things already.
Intellectual creativity, persistence, and a willingness to acquire new skills and problem-solve matter a lot. We think doctoral education is less about executing a task that someone else hands you and more about learning how to identify a new, important problem; develop an appropriate approach to solving it; and explain all of the above and why it matters so that other people can learn from you in the future. Evidence that you can or at least want to do these things is critical. Indications that you can also play well with others and would make a generous, friendly colleague are really important too.
All of this is to say, we do not have any one trait or skill set we look for in prospective students. We strive to be inclusive along every possible dimension. Each person who has joined our group has contributed unique skills and experiences as well as their own personal interests. We want our future students and colleagues to do the same.
Still not sure whether or how your interests might fit with the group? Still have questions? Still reading and just don’t want to stop? Follow the links above for more information. Feel free to send at least one of us an email. We are happy to try to answer your questions and always eager to chat.
This post was co-authored by Benjamin Mako Hill and Aaron Shaw. We wrote it following a conversation with the CSCW 2018 papers chairs. At their encouragement, we put together this proposal that we plan to bring to the CSCW town hall meeting. Thanks to Karrie Karahalios, Airi Lampinen, Geraldine Fitzpatrick, and Andrés Monroy-Hernández for engaging in the conversation with us and for facilitating the participation of the CSCW community.
Quantitative methodologists argue that the high rates of false discovery are, among other reasons, a function of common research practices carried out in good faith. Such practices include accidental or intentional p-hacking where researchers try variations of their analysis until they find significant results; a garden of forking paths where researcher decisions lead to a vast understatement of the number of true “researcher degrees of freedom” in their research designs; the file-drawer problem which leads only statistically significant results to be published; and underpowered studies, which make it so that only overstated effect sizes can be detected.
To the degree that much of CSCW and HCI use the same research methods and approaches as these other social scientific fields, there is every reason to believe that these issues extend to social computing research. Of course, given that replication is exceedingly rare in HCI, HCI researchers will rarely even find out that a result is wrong.
To date, no comprehensive set of solutions to these issues exists. However, scholarly communities can take steps to reduce the threat of false discovery. One set of approaches to doing so involves the introduction of changes to the way quantitative studies are planned, executed, and reviewed. We want to encourage the CSCW community to consider supporting some of these practices.
Among the approaches developed and adopted in other research communities, several involve breaking up research into two distinct stages: a first stage in which research designs are planned, articulated, and recorded; and a second stage in which results are computed following the procedures in the recorded design (documenting any changes). This stage-based process ensures that designs cannot shift in ways that shape findings without some clear acknowledgement that such a shift has occurred. When changes happen, adjustments can sometimes be made in the computation of statistical tests. Readers and reviewers of the work can also have greater awareness of the degree to which the statistical tests accurately reflect the analysis procedures or not and adjust their confidence in the findings accordingly.
Versions of these stage-based research designs were first developed in biomedical randomized controlled trials (RCTs) and are extremely widespread in that domain. For example, pre-registration of research designs is now mandatory for NIH funded RCTs and several journals are reviewing and accepting or rejecting studies based on pre-registered designs before results are known.
A proposal for CSCW
In order to address the challenges posed by false discovery, CSCW could adopt a variety of approaches from other fields that have already begun to do so. These approaches entail more or less radical shifts to the ways in which CSCW research gets done, reviewed, and published.
As a starting point, we want to initiate discussion around one specific proposal that could be suitable for a number of social computing studies and would require relatively little in the way of changes to the research and reviewing processes used in our community.
Drawing from a series of methodological pieces in the social sciences (, , ), we propose a method based on split-sample designs that would be entirely optional for CSCW authors at the time of submission.
Essentially, authors who chose to do so could submit papers which were written—and which will be reviewed and revised—based on one portion of their dataset with the understanding that the paper would be published using identical analytic methods also applied to a second, previously un-analyzed portion of the dataset. Authors submitting under this framework would choose to have their papers reviewed, revised and resubmitted, and accepted or rejected based on the quality of the research questions, framing, design, execution, and significance of the study overall. The decision would not be based on the statistical significance of final analysis results.
The idea follows from the statistical technique of “cross validation,” in which an analysis is developed on one subset of data (usually called the “training set”) and then replicated on at least one other subset (the “test set”).
To conduct a project using this basic approach, a researcher would:
Randomly partition their full dataset into two (or more) pieces.
Design, refine, and complete their analysis using only one piece identified as the training sample.
Undergo the CSCW review process using the results from this analysis of the training sample.
If the submission receives a decision of “Revise and Resubmit,” authors would then make changes to the analysis of the training sample as requested by ACs and reviewers in the current normal way.
If the paper is accepted for publication, the authors would then (and only then) run the final version of the analysis using another piece of their data identified as the test sample and publish those results in the paper.
We expect that authors would also publish the training set results used during review in the online supplement to their paper uploaded to the ACM Digital Library.
Like any other part part of a paper’s methodology, the split sample procedure would be documented in appropriate parts of the paper.
We are unaware of prior work in social computing that has applied this process. Researchers in data mining, machine learning, and related fields of computer science use cross-validation all the time, they do so differently in order to solve distinct problems (typically related to model overfitting).
The main benefits of this approach (discussed in much more depth in the references at the beginning of this section) would be:
Heightened reliability and reproducibility of the analysis.
Reduced risk that findings reflect spurious relationships, p-hacking, researcher or reviewer degrees of freedom, or other pitfalls of statistical inference common in the analysis of behavioral data—i.e., protection against false discovery.
A procedural guarantee that the results do not determine the publication (or not) of the work—i.e., protection against publication bias.
The most salient risk from the approach is that results might change when authors run the final analysis on the test set. In the absence of p-hacking and similar issues, such changes will usually be small and will mostly impact the magnitude of effects estimates and their associated standard errors. However, some changes might be more dramatic. Dealing with changes of this sort would be harder for authors and reviewers and would potentially involve something along the lines of the shepherding that some papers receive now.
Let’s talk it over!
This blog post is meant to spark a wider discussion. We hope this can happen during CSCW this year and beyond. We believe the procedure we have proposed would enhance the reliability of our work and is workable in CSCW because it involves narrow changes to the way that quantitative CSCW research and reviewing is usually conducted. We also believe this procedure would serve the long term interests of the HCI and social computing research community. CSCW is a leader in building better models of scientific publishing within HCI through the R&R process, eliminated page limits, the move to PACM, and more. We would like to extend this spirit to issues of reproducibility and publication bias. We are eager to discuss our proposal and welcome suggestions for changes.
Leaders and scholars of online communities tend of think of community growth as the aggregate effect of inexperienced individuals arriving one-by-one. However, there is increasing evidence that growth in many online communities today involves newcomers arriving in groups with previous experience together in other communities. This difference has deep implications for how we think about the process of integrating newcomers. Instead of focusing only on individual socialization into the group culture, we must also understand how to manage mergers of existing groups with distinct cultures. Unfortunately, online community mergers have, to our knowledge, never been studied systematically.
To better understand mergers, I spent six months in 2017 conducting ethnographic participant observation in two World of Warcraft raid guilds planning and undergoing mergers. The results—visible in the attendance plot below—shows that the top merger led to a thriving and sustainable community while the bottom merger led to failure and the eventual dissolution of the group. Why did one merger succeed while the other failed? What can managers of other communities learn from these examples?
In my new paper that will be published in the Proceedings of of the ACM Conference on Computer-supported Cooperative Work and Social Computing (CSCW) and that I will present in New Jersey next month, my coauthors and I try to answer these questions.
In my research setting, World of Warcraft (WoW), players form organized groups called “guilds” to take on the game’s toughest bosses in virtual dungeons that are called “raids.” Raids can be extremely challenging, and they require a large number of players to be successful. Below is a video demonstrating the kind of communication and coordination needed to be successful as a raid team in WoW.
Because participation in a raid guild requires time, discipline, and emotional investment, raid guilds are constantly losing members and recruiting new ones to resupply their ranks. One common strategy for doing so is arranging formal mergers. My study involved following two such groups as they completed mergers. To collect data for my study, I joined both groups, attended and recorded all activities, took copious field notes, and spent hours interviewing leaders.
Although I did not anticipate the divergent outcomes shown in the figure above when I began, I analyzed my data with an eye toward identifying themes that might point to reasons for the success of one merger and the failure of the other. The answers that emerged from my analysis suggest that the key differences that led one merger to be successful and the other to fail revolved around differences in the ways that the two mergers managed organizational culture. This basic insight is supported by a body of research about organizational culture in firms but seem to have not made it onto the radar of most members or scholars of online communities. My coauthors and I think more attention to the role that organizational culture plays in online communities is essential.
We found evidence of cultural incompatibility in both mergers and it seems likely that some degree of cultural clashes is inevitable in any merger. The most important result of our analysis are three observations we drew about specific things that the successful merger did to effectively manage organizational culture. Drawn from our analysis, these themes point to concrete things that other communities facing mergers—either formal or informal—can do.
First, when planning mergers, groups can strategically select other groups with similar organizational culture. The successful merger in our study involved a carefully planned process of advertising for a potential merger on forums, testing out group compatibility by participating in “trial” raid activities with potential guilds, and selecting the guild that most closely matched their own group’s culture. In our settings, this process helped prevent conflict from emerging and ensured that there was enough common ground to resolve it when it did.
Second, leaders can plan intentional opportunities to socialize members of the merged or acquired group. The leaders of the successful merger held community-wide social events in the game to help new members learn their community’s norms. They spelled out these norms in a visible list of rules. They even included the new members in both the brainstorming and voting process of changing the guild’s name to reflect that they were a single, new, cohesive unit. The leaders of the failed merger lacked any explicitly stated community rules, and opportunities for socializing the members of the new group were virtually absent. Newcomers from the merged group would only learn community norms when they broke one of the unstated social codes.
Third and finally, our study suggested that social activities can be used to cultivate solidarity between the two merged groups, leading to increased retention of new members. We found that the successful guild merger organized an additional night of activity that was socially-oriented. In doing so, they provided a setting where solidarity between new and existing members can cultivate and motivate their members to stick around and keep playing with each other — even when it gets frustrating.
Our results suggest that by preparing in advance, ensuring some degree of cultural compatibility, and providing opportunities to socialize newcomers and cultivate solidarity, the potential for conflict resulting from mergers can be mitigated. While mergers between firms often occur to make more money or consolidate resources, the experience of the failed merger in our study shows that mergers between online communities put their entire communities at stake. We hope our work can be used by leaders in online communities to successfully manage potential conflict resulting from merging or acquiring members of other groups in a wide range of settings.
Much more detail is available our paper which will be published open access and which is currently available as a preprint.
Couchsurfing and Airbnb are websites that connect people with an extra guest room or couch with random strangers on the Internet who are looking for a place to stay. Although Couchsurfing predates Airbnb by about five years, the two sites are designed to help people do the same basic thing and they work in extremely similar ways. They differ, however, in one crucial respect. On Couchsurfing, the exchange of money in return for hosting is explicitly banned. In other words, couchsurfing only supports the social exchange of hospitality. On Airbnb, users must use money: the website is a market on which people can buy and sell hospitality.
The figure above compares the number of people with at least some trust or verification on both Couchsurfing and Airbnb based on when each user signed up. The picture, as I have argued elsewhere, reflects a broader pattern that has occurred on the web over the last 15 years. Increasingly, social-based systems of production and exchange, many like Couchsurfing created during the first decade of the Internet boom, are being supplanted and eclipsed by similar market-based players like Airbnb.
In a paper led by Max Klein that was recently published and will be presented at the ACM Conference on Computer-supported Cooperative Work and Social Computing (CSCW) which will be held in Jersey City in early November 2018, we sought to provide a window into what this change means and what might be at stake. At the core of our research were a set of interviews we conducted with “dual-users” (i.e. users experienced on both Couchsurfing and Airbnb). Analyses of these interviews pointed to three major differences, which we explored quantitatively from public data on the two sites.
First, we found that users felt that hosting on Airbnb appears to require higher quality services than Couchsurfing. For example, we found that people who at some point only hosted on Couchsurfing often said that they did not host on Airbnb because they felt that their homes weren’t of sufficient quality. One participant explained that:
“I always wanted to host on Airbnb but I didn’t actually have a bedroom that I felt would be sufficient for guests who are paying for it.”
An another interviewee said:
“If I were to be paying for it, I’d expect a nice stay. This is why I never Airbnb-hosted before, because recently I couldn’t enable that [kind of hosting].”
We conducted a quantitative analysis of rates of Airbnb and Couchsurfing in different cities in the United States and found that median home prices are positively related to number of per capita Airbnb hosts and a negatively related to the number of Couchsurfing hosts. Our exploratory models predicted that for each $100,000 increase in median house price in a city, there will be about 43.4 more Airbnb hosts per 100,000 citizens, and 3.8 fewer hosts on Couchsurfing.
A second major theme we identified was that, while Couchsurfing emphasizes people, Airbnb places more emphasis on places. One of our participants explained:
“People who go on Airbnb, they are looking for a specific goal, a specific service, expecting the place is going to be clean […] the water isn’t leaking from the sink. I know people who do Couchsurfing even though they could definitely afford to use Airbnb every time they travel, because they want that human experience.”
In a follow-up quantitative analysis we conducted of the profile text from hosts on the two websites with a commonly-used system for text analysis called LIWC, we found that, compared to Couchsurfing, a lower proportion of words in Airbnb profiles were classified as being about people while a larger proportion of words were classified as being about places.
Finally, our research suggested that although hosts are the powerful parties in exchange on Couchsurfing, social power shifts from hosts to guests on Airbnb. Reflecting a much broader theme in our interviews, one of our participants expressed this concisely, saying:
“On Airbnb the host is trying to attract the guest, whereas on Couchsurfing, it works the other way round. It’s the guest that has to make an effort for the host to accept them.”
Previous research on Airbnb has shown that guests tend to give their hosts lower ratings than vice versa. Sociologists have suggested that this asymmetry in ratings will tend to reflect the direction of underlying social power balances.
We both replicated this finding from previous work and found that, as suggested in our interviews, the relationship is reversed on Couchsurfing. As shown in the figure above, we found Airbnb guests will typically give a less positive review to their host than vice-versa while in Couchsurfing guests will typically a more positive review to the host.
As Internet-based hospitality shifts from social systems to the market, we hope that our paper can point to some of what is changing and some of what is lost. For example, our first result suggests that less wealthy participants may be cut out by market-based platforms. Our second theme suggests a shift toward less human-focused modes of interaction brought on by increased “marketization.” We see the third theme as providing somewhat of a silver-lining in that shifting power toward guests was seen by some of our participants as a positive change in terms of safety and trust in that guests. Travelers in unfamiliar places often are often vulnerable and shifting power toward guests can be helpful.
Although our study is only of Couchsurfing and Airbnb, we believe that the shift away from social exchange and toward markets has broad implications across the sharing economy. We end our paper by speculating a little about the generalizability of our results. I have recently spoken at much more length about the underlying dynamics driving the shift we describe in my recent LibrePlanet keynote address.
Every CASBS study is labeled with a list of “ghosts” who previously occupied the study. This year, I’m spending the year in Study 50 where I’m haunted by an incredible cast that includes many people whose scholarship has influenced and inspired me.
Foremost among this group is Study 50’s third occupant: Claude Shannon.¹
At 21 years old, Shannon’s masters thesis (sometimes cited as the most important masters thesis in history) proved that electrical circuits could encode any relationship expressible in Boolean logic and opened the door to digital computing. Incredibly, this is almost never cited as Shannon’s most important contribution. That came in 1948 when he published a paper titled A Mathematical Theory of Communication which effectively created the field of information theory. Less than a decade after its publication, Aleksandr Khinchin (the mathematician behind my favorite mathematical constant) described the paper saying:
Rarely does it happen in mathematics that a new discipline achieves the character of a mature and developed scientific theory in the first investigation devoted to it…So it was with information theory after the work of Shannon.
As someone whose own research is seeking to advance computation and mathematical study of communication, I find it incredibly propitious to be sharing a study with Shannon.
Although I teach in a communication department, I know Shannon from my background in computing. I’ve always found it curious that, despite the fact Shannon’s 1948 paper is almost certainly the most important single thing ever published with the word “communication” in its title, Shannon is rarely taught in communication curricula is sometimes completely unknown to communication scholars.
In establishing itself under the banner of communication, the discipline staked an academic claim to the entire field of communication theory and research—a very big claim indeed, since communication had already been widely studied and theorized. Peters writes that communication research became “an intellectual Taiwan-claiming to be all of China when, in fact, it was isolated on a small island” (p. 545). Perhaps the most egregious case involved Shannon’s mathematical theory of information (Shannon & Weaver, 1948), which communication scholars touted as evidence of their field’s potential scientific status even though they had nothing whatever to do with creating it, often poorly understood it, and seldom found any real use for it in their research.
In preparation for moving into Study 50, I read a new biography of Shannon by Jimmy Soni and Rob Goodman and was excited to find that Craig—although accurately describing many communication scholars’ lack of familiarity—almost certainly understated the importance of Shannon to communication scholarship.
For example, the book form of Shannon’s 1948 article was published by University Illinois on the urging of and editorial supervision of Wilbur Schramm (one of the founders of modern mass communication scholarship) who was a major proponent of Shannon’s work. Everett Rogers (another giant in communication) devotes a chapter of his “History of Communication Studies”² to Shannon and to tracing his impact in communication. Both Schramm and Rogers built on Shannon in parts of their own work. Shannon has had an enormous impact, it turns out, in several subareas of communication research (e.g., attempts to model communication processes).
Although I find these connections exciting. My own research—like most of the rest of communication—is far from the substance of technical communication processes at the center of Shannon’s own work. In this sense, it can be a challenge to explain to my colleagues in communication—and to my fellow CASBS fellows—why I’m so excited to be sharing a space with Shannon this year.
Upon reflection, I think it boils down to two reasons:
Shannon’s work is both mathematically beautiful and incredibly useful. His seminal 1948 article points to concrete ways that his theory can be useful in communication engineering including in compression, error correcting codes, and cryptography. Shannon’s focus on research that pushes forward the most basic type of basic research while remaining dedicated to developing solutions to real problems is a rare trait that I want to feature in my own scholarship.
Shannon was incredibly playful. Shannon played games, juggled constantly, and was always seeking to teach others to do so. He tinkered, rode unicycles, built a flame-throwing trumpet, and so on. With Marvin Minsky, he invented the “ultimate machine”—a machine that’s only function is to turn itself off—which he kept on his desk.
I have no misapprehension that I will accomplish anything like Shannon’s greatest intellectual achievements during my year at CASBS. I do hope to be inspired by Shannon’s creativity, focus on impact, and playfulness. In my own little ways, I hope to build something at CASBS that will advance mathematical and computational theory in communication in ways that Shannon might have appreciated.
Incredibly, the year that Shannon was in Study 50, his neighbor in Study 51 was Milton Friedman. Two thoughts: (i) Can you imagine?! (ii) I definitely chose the right study!
Rogers book was written, I found out, during his own stint at CASBS. Alas, it was not written in Study 50.
Forming: Group members get to know each other and define their task.
Storming: Through argument and disagreement, power dynamics emerge and are negotiated.
Norming: After conflict, groups seek to avoid conflict and focus on cooperation and setting norms for acceptable behavior.
Performing: There is both cooperation and productive dissent as the team performs the task at a high level.
Fortunately for organizational science, 1965 was hardly the last stage of development for Tuckman’s theory!
Twelve years later, Tuckman suggested that adjourning or mourning reflected potential fifth stages (Tuckman and Jensen 1977). Since then, other organizational researchers have suggested other stages including transforming and reforming (White 2009), re-norming (Biggs), and outperforming (Rickards and Moger 2002).
What does the future hold for this line of research?
The good news is that despite the active stream of research producing new stages that end or rhyme with -orming, there are tons of great words left!
For example, stages in a group’s development might include:
Scorning: In this stage, group members begin mocking each other!
Misinforming: Groups that reach this stage start producing fake news.
Shoehorning: These groups try to make their products fit into ridiculous constraints.
Chloroforming: Groups become languid and fatigued?
One benefit of keeping our list in the wiki is that the organizational research community can use it to coordinate! If you are planning to use one of these terms—or if you know of a paper that has—feel free to edit the page in our wiki to “claim” it!
This year, there are seven partner locations where local students livestream the activities from Duke and learn from local computational social scientists. Both of our universities are among the partner locations.
At the University of Washington, Kaylea and Charlie have both been accepted as participants in the UW summer institute. At Northwestern University, Jeremy is helping to organize SICSS Chicago.
Much of the work that we do in the Community Data Science Collective could be considered computational social science, and we are excited about the potential for computational methods in social science. This is a great program for helping to disseminate computational social science approaches and train the next generation of computational social scientists. The Community Data Science Collective is happy to be a sponsor of the Chicago partner location.
UNC has has every reason to be excited. Sayamindu has been making our research collective look good for several years. Much of this is obvious in the pile of papers and awards he’ s built. In less visible roles, Sayamindu has helped us build infrastructure, mentored graduate and undergraduate students in the group, and has basically just been joy to have around.
Those of us that work in the Community Data Lab at UW is going to miss having Sayamindu around. Chapel Hill is very, very lucky to have him.