Genealogy That Counts: Using Content Analysis to Explore the Evolution of Persuasive Cartography
Bibliographic record
Abstract
Maps are often used for persuasive purposes. Yet little is known about whether or not different persuasive map producers use similar cartographic techniques or about how such techniques have evolved through time. This article explores the genealogy of persuasive map design by analyzing 256 such maps published since 1800. Quantitative content analysis is used to break down each map among 190 contextual, data, design, and layout variables. Cross-tabulations and descriptive statistics are used to test whether and how the characteristics of persuasive maps have changed over time and differ by type of producer. The results illustrate that although some persuasive techniques have changed over time and by producer, most techniques are recurring and used by all types of producers. The results imply that different techniques of persuasive cartography are largely timeless, regardless of producer or medium. It is also argued that content analysis may prove a useful method for other research in map genealogy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".