Visualizing information worldwide a panel proposal to the 2015 ASIS&T annual meeting sponsored by: SIG‐VIS, SIG‐III, & SIG‐HFIS
Bibliographic record
Abstract
ABSTRACT Twelve scholars based at information programs worldwide have recently participated in research that asks: How is the concept of information visualized in my community and beyond? The study employed an arts‐informed, visual methodology and the draw‐and‐write technique to stimulate local and global conversations about the pictorial nature of information in society. The work has generated new insights into information as a visual phenomenon and generated an archive of “iSquare” images to be used for information research, education, and practice. As a contribution to the conference theme of “Impact on Society,” this panel introduces the project, describes its technical infrastructure, highlights emerging social scientific and artistic outcomes, and reports cross‐cultural discoveries. The multimedia and interactive session will include in‐person presentations, short videos from collaborators overseas, an expert discussant, dialogue with the audience, and an art exhibition.
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 | 0.018 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.076 | 0.014 |
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".