The nice thing about context is that everyone has it

By Nick Seaver. 2015

123 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems, Transportation

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Dong Nguyen
2. Helen Z. Margetts
3. Madeleine Clare Elish
4. Gina Neff
5. D. boyd

In Information Systems

1. Bertie Vidgen
2. Anissa Tanweer
3. Brittany Fiore-Gartland
4. Dong Nguyen
5. Helen Z. Margetts
6. Madeleine Clare Elish
7. D. boyd
8. Rebekah Tromble

In Transportation

1. Madeleine Clare Elish
2. D. boyd

Citations in the corpus

• Critique and Contribute: A Practice-Based Framework for Improving Critical Data Studies and Data Science (2017, background): “center of big data critiques, context is also a central concept for the work of human computer interaction, ubiquitous computing, and data science.(27,28) Seaver’s study of ‘‘context-aware’’ music recommender systems revealed”
• Introducing CAD: the Contextual Abuse Dataset (2021, background): “Detection systems which do not account for context are likely to be less applicable in the real-world, where nearly all content appears in a certain context (Seaver, 2015).”
• Situating methods in the magic of Big Data and AI (2018, background): “Although the epistemological frameworks appear incongruent, the ways of building knowledge have striking parallels (Seaver, 2015a).”