Demo Data from EDBT 2026 / reviewers in the wild / expert
Jodie Jenkinson
dblp:29/8748
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2026
0000-0002-4066-9732ORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer graphics and multimedia
1 paper |
Visualization and visual analytics · 100% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Visualization and visual analytics › data storytelling
data journalism |
1.0 | 1 | 2026 | Deconstructing Implicit Beliefs in Visual Data Journalism: Unstable Meanings Behind Data as Truth & Design for Insight · IEEE Trans. Vis. Comput. Graph. 2026 |
Visualization and visual analytics
data storytelling |
1.0 | 1 | 2026 | Deconstructing Implicit Beliefs in Visual Data Journalism: Unstable Meanings Behind Data as Truth & Design for Insight · IEEE Trans. Vis. Comput. Graph. 2026 |
Methods — techniques the papers use, named apart from their topics
qualitative interview study · 1.0genealogical analysis · 1.0deconstruction · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Deconstructing Implicit Beliefs in Visual Data Journalism: Unstable Meanings Behind Data as Truth & Design for InsightabstractWe conduct a deconstructive reading of a qualitative interview study with 17 visual data journalists from newsrooms across the globe. We borrow a deconstruction approach from literary critique to explore the instability of meaning in language and reveal implicit beliefs in words and ideas. Through our analysis we surface two sets of opposing implicit beliefs in visual data journalism: objectivity/subjectivity and humanism/mechanism. We contextualize these beliefs through a genealogical analysis, which brings deconstruction theory into practice by providing a historic backdrop for these opposing perspectives. Our analysis shows that these beliefs held within visual data journalism are not self-enclosed but rather a product of external societal forces and paradigm shifts over time. Through this work, we demonstrate how thinking with critical theories such as deconstruction and genealogy can reframe "success" in visual data storytelling and diversify visualization research outcomes. These efforts push the ways in which we as researchers produce domain knowledge to examine the sociotechnical issues of today's values towards datafication and data visualization. All supplemental materials for this work are available at osf.io/5fr48. Ke Er Amy Zhang, Jodie Jenkinson, Laura A. Garrison |
IEEE Trans. Vis. Comput. Graph. | 2 |