Understanding through Structure: The Challenges of Information and Navigation Architecture in Cybercartography
Bibliographic record
Abstract
The information-rich environments suggested by the concept of cybercartography present new challenges to the design and display of interactive geographically oriented information. These new challenges revolve around two basic issues: information architecture and information navigation models. A variety of information architectures are discussed in this article, along with an analysis of topologies such as the hierarchy (tree), linear sequence, matrix (grid), web-like, or hybrid. A three-tier information architecture model is proposed that varies in information granularity. The high-granularity level includes information units such as maps, articles, images, animations, video clips, and data graphs. The medium-granularity level includes integrated functional information units that are topical, task oriented, audience specific, or a hybrid. At such a level of information units, maps are no longer a stand-alone element. Finally, the low-granularity level includes top-level information architecture linking the various functional units. The advantage of these three granularity levels is that they enable an adaptable information architecture that can accommodate the addition of new content composed of the basic elements. While the way users navigate such an information architecture can be prescribed by the information structure, we demonstrate how navigation schemes can be independent of the information architecture and offer the user a greater diversity of interaction.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| 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".