Bibliographic record
Abstract
Information visualization, aided by ever more accessible computational resources, continues to grow in popularity and significance. The capability to generate complex imagery by computer is often necessary but not always sufficient to gain the desired insight. The success of a visual representation in a given context may be affected by many variables, not the least of which is the individual user's experience. Even if a precise relationship could be found between context and "best" visual representation, the complete articulation of a context is practically impossible. In other fields, this is known as sensitive dependence to initial conditions. A more feasible alternative is to begin with an incomplete articulation of a context and allow the user to interactively develop and refine it. Although most computer interfaces for information visualization tools are predominantly verbal, a predominantly visual interface can have significant advantages. Such an interface allows users to avoid the usual translations between visual and verbal modes and it removes users' need for a specialized visualization vocabulary. A visual interface can also shift the focus of the visualization process from the data towards the user These ideas are discussed in the context of a prototype tool, the design of which is illustrated with an example, and the evaluation of which has provided many positive results.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.018 | 0.008 |
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 source (direct Gemma or distilled Codex), 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".