Online Issue Mapping of International News and Information Design
Bibliographic record
Abstract
Online issue mapping uses interactive network or topic maps to relate information sources to each other and to their respective uses of key terms. This strategy prioritizes the ability of visualization systems to show complex data that change over time. Our research applies combinations of software in a cross-disciplinary technique well suited to the information dynamics of the modern world. In an attempt to combine network maps over time, turning them from snapshots to a chronologically sensitive visualization system, we have undertaken two projects: 1. Mapping of the North Korean English Language New Media Space, available for free through Google News. The North Korean mapping project is part of an effort to study conflict through new media. 2. Mapping of the Information Design Research Community, as it is presented in new media. This second project has examined information design, using network maps over time and textual analysis software to understand the information design and visualization research community as it is presented in new media. Our research is part of new developments in network mapping that aim to elucidate the operations of academic and professional institutions and organizations, blogs, news media, and the public diplomacy of states. Our mapping is dynamic in two senses: each map is a snapshot of content that is constantly changing, and the SVG or cluster map representations of the maps are interactive, allowing readers to actively study the visualizations by zooming, selecting elements for further information, and following links.
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.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".