A Summary of Papers in the Volume.
| Section | Author(s) | Title | Research Question | Methods | Key Findings/Contributions |
|---|---|---|---|---|---|
| Algorithmic organizing and data scientists | Hopf, Joshi, Shollo, and Stelmaszak | Data-based craft: How data scientists craft their data, models, and products | How do data scientists iteratively shape “materials” (data), “tools” (algorithms), and “products” (analytics outputs) in a craft-like manner? | 65 in-depth interviews across 25 organizations; grounded theory | Introduces “data-based craft,” showing how data scientists continuously rework data, refine algorithms, and deliver products that remain in flux – challenging static views of technical craft |
| Valadão, Glaser, and Hannigan | Assembling Frankensteins: How data scientists stitch together provisional artifacts to generate novel insights | In what ways do data scientists create and refine algorithms as evolving “assemblages” or “Frankensteins,” relying on protean tools and communal knowledge? | Multi-method qualitative study (interviews, observation of industry events, archival data) | Depicts how data scientists combine code snippets, libraries, and data sets in improvised ways, emphasizing the ad hoc, iterative nature of building algorithms, rather than a linear, finalized design process | |
| Transforming organizations for algorithmic readiness | Plesner and Justesen | Making organizations algorithm-ready: Algorithmic organizing through techno-organizational scripts | What redesigns and role shifts occur as an organization prepares for and integrates an algorithm, and how do “techno-organizational scripts” evolve in practice? | Ethnographic fieldwork in a public debt collection center | Shows that implementing an algorithm requires iterative “rescripting” of both the technology and existing roles, highlighting tensions between efficiency mandates and professional discretion |
| Washington | Machine-readable legitimacy: An ethnography of regulatory technology | How do continuous data flows (RegTech) reshape financial compliance and the process of securing legitimacy from regulators? | Longitudinal ethnography of industry associations and U.S. regulators (2013–2020) | Develops “machine-readable legitimacy,” revealing that real-time data pipelines move compliance from a periodic, document-based event to an ongoing, algorithmic process negotiated among vendors, industry, and regulators | |
| Opening up algorithmic encounters | Kostuj and Trittin-Ulbrich | Making sense of glitches? Exploring cultural producers’ understandings of and interactions with the Instagram algorithm | How do cultural producers (journalists, artists, activists) interpret algorithmic “glitches,” and what do these moments reveal about platform power and user strategies? | Online ethnography (240 hours), semi-structured interviews with cultural producers | Demonstrates that perceived glitches (e.g., shadowbanning) prompt creators to adapt content, self-censor, and mobilize collective resistance, exposing hidden biases and power asymmetries in platform algorithms |
| Sharma and Aristidou | Human-AI coordination in extreme contexts: Overcoming trust and agency concerns | How can humanitarian “super-teams” integrate AI effectively in life-or-death crises while navigating trust (black-box fears) and agency (automation risks)? | Archival data, multi-crisis comparative analysis (Gaza, Kenya, Nepal), process tracing | Identifies paradoxical tensions around trust and human agency; proposes “coordinative divergence” (broad inclusion) and “coordinative convergence” (unified goals) as twin practices that help AI teams function under urgent, high-stakes conditions | |
| Advancing methodologies for the study of algorithmic organizing | Spencer and Kim | Interpreting the inscrutable: Ethnographic approaches to studying the development of machine learning models | How can researchers use a “data work”-centered ethnography to uncover how ML models are shaped by local hierarchies, biases, and resource constraints? | Comparative ethnography in two teaching hospitals (China and the Netherlands); observation of data collection, annotation, and recalibration | Highlights how organizational politics and iterative data labeling processes embed bias and tacit knowledge into ML models, offering a “data work” lens to demystify algorithmic inscrutability |
| Timmer, Wrona, and Reinecke | Exploring algorithmic assemblages through multimodal inquiry | Why and how should researchers adopt multimodal methods (verbal, visual, embodied) to study algorithmic organizing and its sociomaterial “folding?” | Proposal of a multimodal qualitative framework; illustration via an IT consultancy case; focus on non-verbal cues, code logs, and emotional dynamics | Provides a 10-principle methodology for capturing the often-hidden interactions (visual, embodied, emotional) that shape how algorithms are adopted, resisted, or transformed in organizational settings |
| Section | Author(s) | Title | Research Question | Methods | Key Findings/Contributions |
|---|---|---|---|---|---|
| Algorithmic organizing and data scientists | Hopf, Joshi, Shollo, and Stelmaszak | Data-based craft: How data scientists craft their data, models, and products | How do data scientists iteratively shape “materials” (data), “tools” (algorithms), and “products” (analytics outputs) in a craft-like manner? | 65 in-depth interviews across 25 organizations; grounded theory | Introduces “data-based craft,” showing how data scientists continuously rework data, refine algorithms, and deliver products that remain in flux – challenging static views of technical craft |
| Valadão, Glaser, and Hannigan | Assembling Frankensteins: How data scientists stitch together provisional artifacts to generate novel insights | In what ways do data scientists create and refine algorithms as evolving “assemblages” or “Frankensteins,” relying on protean tools and communal knowledge? | Multi-method qualitative study (interviews, observation of industry events, archival data) | Depicts how data scientists combine code snippets, libraries, and data sets in improvised ways, emphasizing the ad hoc, iterative nature of building algorithms, rather than a linear, finalized design process | |
| Transforming organizations for algorithmic readiness | Plesner and Justesen | Making organizations algorithm-ready: Algorithmic organizing through techno-organizational scripts | What redesigns and role shifts occur as an organization prepares for and integrates an algorithm, and how do “techno-organizational scripts” evolve in practice? | Ethnographic fieldwork in a public debt collection center | Shows that implementing an algorithm requires iterative “rescripting” of both the technology and existing roles, highlighting tensions between efficiency mandates and professional discretion |
| Washington | Machine-readable legitimacy: An ethnography of regulatory technology | How do continuous data flows (RegTech) reshape financial compliance and the process of securing legitimacy from regulators? | Longitudinal ethnography of industry associations and U.S. regulators (2013–2020) | Develops “machine-readable legitimacy,” revealing that real-time data pipelines move compliance from a periodic, document-based event to an ongoing, algorithmic process negotiated among vendors, industry, and regulators | |
| Opening up algorithmic encounters | Kostuj and Trittin-Ulbrich | Making sense of glitches? Exploring cultural producers’ understandings of and interactions with the Instagram algorithm | How do cultural producers (journalists, artists, activists) interpret algorithmic “glitches,” and what do these moments reveal about platform power and user strategies? | Online ethnography (240 hours), semi-structured interviews with cultural producers | Demonstrates that perceived glitches (e.g., shadowbanning) prompt creators to adapt content, self-censor, and mobilize collective resistance, exposing hidden biases and power asymmetries in platform algorithms |
| Sharma and Aristidou | Human- | How can humanitarian “super-teams” integrate | Archival data, multi-crisis comparative analysis (Gaza, Kenya, Nepal), process tracing | Identifies paradoxical tensions around trust and human agency; proposes “coordinative divergence” (broad inclusion) and “coordinative convergence” (unified goals) as twin practices that help | |
| Advancing methodologies for the study of algorithmic organizing | Spencer and Kim | Interpreting the inscrutable: Ethnographic approaches to studying the development of machine learning models | How can researchers use a “data work”-centered ethnography to uncover how | Comparative ethnography in two teaching hospitals (China and the Netherlands); observation of data collection, annotation, and recalibration | Highlights how organizational politics and iterative data labeling processes embed bias and tacit knowledge into |
| Timmer, Wrona, and Reinecke | Exploring algorithmic assemblages through multimodal inquiry | Why and how should researchers adopt multimodal methods (verbal, visual, embodied) to study algorithmic organizing and its sociomaterial “folding?” | Proposal of a multimodal qualitative framework; illustration via an | Provides a 10-principle methodology for capturing the often-hidden interactions (visual, embodied, emotional) that shape how algorithms are adopted, resisted, or transformed in organizational settings |
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