Design Patterns for Data-Driven News Articles
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Technological advancements have resulted in great shifts in the production and consumption of news articles. This, in turn, lead to the requirement of new educational and practical frameworks. In this paper, we present a classification of data-driven news articles and related design patterns defined to describe their visual and textual components. Through the analysis of 162 data-driven news articles collected from news media, we identified five types of articles based on the level of data involvement and narrative complexity: Quick Update, Briefing, Chart Description, Investigation, and In-depth Investigation. We then identified 72 design patterns to understand and construct data-driven news articles. To evaluate this approach, we conducted workshops with 23 students from journalism, design, and sociology who were newly introduced to the subject. Our findings suggest that our approach can be used as an out-of-box framework for the formulation of plans and consideration of details in the workflow of data-driven news creation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it