Many experimental studies of team performance involve teams working on one specific task or at most a handful of tasks; thus, it is natural to ask how the results of any such study generalize to tasks other than the one(s) studied. Unfortunately, this simple question is generally impossible to answer due to the absence of commensurability among tasks: although many taxonomies for classifying tasks have been proposed, it is not possible to say comprehensively, consistently, and quantitatively how similar or different any task is to any other. We therefore cannot in general say when we should expect to find different or similar results for teams working on different tasks. Here, we address the commensurability problem by introducing a multidimensional framework that synthesizes existing taxonomies and defines a between-task distance measure in terms of 24 taxonomic dimensions (e.g., “degree of creativity required,” “amount of physical effort required,” “extent of demonstrable correctness”). We then use these dimensions to label 102 tasks sourced from an interdisciplinary social science literature. Finally, we use this repository of tasks to demonstrate some of the benefits of our multidimensional approach: by shedding light on the key underlying dimensions for every team task, our paradigm makes it easier to integrate ostensibly “contradictory” findings, identify contingencies for theories, and efficiently sample tasks for future research experiments.

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By identifying the socio-technical conditions required for teams to work effectively remotely, the Distance Matters framework has been influential in CSCW since its introduction in 2000. Advances in collaboration technology and practices have since brought teams increasingly closer to achieving these conditions. This paper presents a ten-month ethnography in a remote organization, where we observed that despite exhibiting excellent remote collaboration, teams paradoxically struggled to collaborate across team boundaries. We extend the Distance Matters framework to account for inter-team collaboration, arguing that challenges analogous to those in the original intra-team framework --- common ground, collaboration readiness, collaboration technology readiness, and coupling of work --- persist but are actualized differently at the inter-team scale. Finally, we identify a fundamental tension between the intra- and inter-team layers: the collaboration technology and practices that help individual teams thrive (e.g., adopting customized collaboration software) can also prompt collaboration challenges in the inter-team layer, and conversely the technology and practices that facilitate inter-team collaboration (e.g., strong centralized IT organizations) can harm practices at the intra-team layer. The addition of the inter-team layer to the Distance Matters framework opens new opportunities for CSCW, where balancing the tension between team and organizational collaboration needs will be a critical technological, operational, and organizational challenge for remote work in the coming decades.

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A jury of one’s peers is a prominent way to adjudicate disputes and is increasingly used in participatory governance online. The fairness of this approach rests on the assumption that juries are consistent: that the same jury would hand down similar judgments to similar cases. However, prior literature suggests that social influence would instead cause early interactions to cascade into different judgments for similar cases. In this paper, we report an online experiment that changes participants’ pseudonyms as they appear to collaborators, temporarily masking a jury’s awareness that they have deliberated together before. This technique allows us to measure consistency by reconvening the same jury on similar cases. Counter to expectation, juries are equally consistent as individuals, a result that is “good for democracy.” But this consistency arises in part due to group polarization, as consensus develops by hardening initial majority opinions. Furthermore, we find that aggregating groups’ perspectives without deliberation erodes consistency.

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Gender bias has been a pervasive issue in U.S. political coverage since women gained the franchise in 1920. Women are routinely targeted for their appearance, likeability, and familial qualities—characteristics for which men are rarely scrutinized. As a way to help journalists and editors identify and correct gender-biased language in political reporting, we introduce “Disarming Loaded Words” (DLW) as a new solution to this issue. DLW is a computational tool, backed by both machine learning and human expert curation, that tags potentially biased words in a document and provides feedback and context for why a word may be problematic. DLW provides a minimally disruptive way to nudge journalists toward understanding unconscious biases. It was successfully prototyped as a standalone Google Docs add-on, and the code for its model, interface, and API are now open-source and available for use on other platforms.

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