Bibliographic record
Abstract
The extent to which data visualizations are used, and the quality of these visualizations, has consistently been shown to influence human decision-making relative to static (non-visual) presentations of findings or ideas (Boldosova & Luoto, 2019; El-Wakeel et al., 2020; Liem et al., 2020). Why are visualizations so impactful? Likely because most decision-makers do not want to sort through spreadsheets or read a novel-length narrative to understand what is important—they want it straight and quick. They want color, novelty, storytelling, and interactivity (Dykes, 2020; Kostelnick, 2016; Kosara & MacKinley, 2013). This is the purpose of data storytelling: to literally tell a story about the data analyses to, in some way, impart knowledge or affect change among the audience. Data and data analysis are never neutral—they are always political–and storytelling is how the data analyst can attempt to influence how data findings are perceived by the audience. This paper discusses the basis of data storytelling and why it is important for creating a narrative around data visualizations that compels readers and viewers to act upon findings. It then discusses (in the form of a reflective discussion) how the art of data storytelling may be improved and activated to promote social justice themes by reflecting on the effectiveness of storytelling in hip hop music.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".