From numbers to discourse and action: Visualizing meaning through data as it happens
Bibliographic record
Abstract
Abstract The use of data to influence decisions has become near ubiquitous in fields from education to public policy. It has become an instrument to confer validation to policy crafted by those in power. New initiatives to make data widely available have led to new strategies for interpreting and representing derived meaning. Data visualization is one method of interpreting vast amounts of numerical data organized in tabular form. In post-industrialized American culture, having more is often considered as being better than less, but new ways to use macro ideas, termed ‘data visualizations’, can inform individual narratives with other qualities. We illustrate these qualities with data visualizations of cultural phenomena and real-time mapping of the We Are Data (WAD) website to show immediate meaning and emotional response in relationships that emerge in virtual locations. Visualized data, we argue, can provide an event in which one’s preconceived interpretations can either be confirmed or called into question quickly and visually, and as a result, new knowledge emerges.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".