This book explores the challenges society faces with big data, through the lens of culture rather than social, political or economic trends, as demonstrated in the words we use, the values that underpin our interactions, and the biases and assumptions that drive us. Focusing on areas such as data and language, data and sensemaking, data and power, data and invisibility, and big data aggregation, it demonstrates that humanities research, focussing on cultural rather than social, political or economic frames of reference for viewing technology, resists mass datafication for a reason, and that those very reasons can be instructive for the critical observation of big data research and innovation.
Author(s): Jennifer Edmond, Nicola Horsley, Jörg Lehmann, Mike Priddy
Series: Bloomsbury Studies In Digital Cultures
Edition: 1
Publisher: Bloomsbury Publishing
Year: 2022
Language: English
Commentary: TruePDF
Pages: 193
Tags: Humanities: Research: Data Processing; Digital Humanities; Big Data
Cover
Half title
Series Title
Title
Copyright
Contents
Acknowledgements
1 | Viewing big data through the lens of culture
The KPLEX project
The KPLEX interviews
Knowledge complexity ‘in the wild’
Applying the KPLEX approach to these issues
2 | What do we mean when we talk about data?
3 | Making sense of data
Interpretation in the humanities: Two examples
Digitization and the change of interpretive practices in the humanities
The historical sciences and big data
Numbers and description, narrative and interpretation
Science as a social system: The social construction of meaning
Making sense of big data
4 | Please mind the gap: The problems of information voids in the knowledge discovery process
The dominance of search engines
Ranking and the long tail problem
Cultural heritage institutions: The original custodians of big data
CHIs provide expert services as knowledge gatekeepers
Content versus context
Spacelessness, placelessness and hypertravel
Google as a threat
Benefits of search engines and digital cultural heritage
Beyond the keyword
5 | Data incognita: How do data become hidden?
Hidden by digital obscurity
Hidden by working practices
Hidden by inconsistent methods of description
Hidden by a loss or unavailability of expertise
Hidden by a lack of material resources
Hidden by privacy
The dark side of discoverability
Discovery through cultural heritage institutional involvement in (European) data and research infrastructures
The future should not be hidden
6 | From obscure data to datafied obscurity: The invisibilities of datafication
What you see is what you get
The minoritized material: Corner cases and downward spirals of invisibility
Casting a shadow: A little sharing is a dangerous thing
Knowledge after Google: The agonism of archives and AI
Future invisibilities: Popular music, unmapped terrain and alternative facts
Hypernormalised hypermarkets of big data: Refusing to be cowed
7 | Power through datafication
Language as data
Cultural heritage
The academic field
Conclusion
8 | Expatriates in the land of data: Software tensions as a clash of culture
More questions than answers?
Is software production also a culture?
Cross-cultural competencies for a Digital Age
Index
