Towards a research agenda for visual informatics
Bibliographic record
Abstract
Abstract “Visual Informatics” currently refers to visualization of and interaction with very large data sets, including both text and numeric data, chemical and molecular structures, and genome sequences. As such, it follows the widespread definition of “informatics” as being “the study of the application of computer and statistical techniques to the management of information,” or, more popularly, “computer science + X.” Commercially available visual informatics software, (such as Logical Images' VisualDx) provides real‐time visual decision support for such things as diagnosis of disease. This panel suggests reorienting the concept of informatics towards the human processes that are facilitated by technology. Along these lines, it proposes a broader definition and refocusing of the concept of visual informatics and explores the expansion of the range of research which visual informatics can address. It also looks beyond visual informatics to the larger concept of “media informatics” where information and communication technologies expand to include multiple modalities of interaction within systems, organizations, and cultures. In keeping with the conference theme of synergies between research and practice, this panel will explore a research agenda grounded through the optics of both current “real world” applications being developed in research and industry and theoretical approaches which can enfold these projects within rich conceptual frameworks. The panel format will consist of a brief introduction to the topic by the moderator, followed by several presentations exploring these issues, followed by a summation and discussion. A unique aspect of this panel is that the dialogue among the presenters and their audience will begin well before the conference, through the mechanism of the ASIS&T SIGVIS weblog ( http://informationvisualization.typepad.com ). We encourage participants to review the weblog and add their comments to the entries.
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 | 0.052 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.009 | 0.035 |
| Scholarly communication | 0.041 | 0.053 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.020 | 0.023 |
| Insufficient payload (model declined to judge) | 0.026 | 0.007 |
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".