Bibliographic record
Abstract
Maps are often used in the promotion of specific forms of political and social doctrine. Some such maps, which I call “maps in the wild,” do not serve the traditional purposes of maps but, rather, exist as symbols themselves, much like corporate logos, communicating meaning and evoking emotional responses. Typical maps in the wild include global representations and outline maps of political units. The use of map images as iconic symbols in political discourse serves to prompt a number of abstract ideas, such as trust, dominion, and spatiality of political philosophies, and a formal examination of the images can reveal interesting differences among the camps using them. In this article, I report on an examination of recent American campaign materials, political advertising, and popular political graphics. The content analysis reflects a polarized society: materials promoting Republican and so-called conservative candidates or values tend to feature images of the United States, while those promoting Democratic or “liberal” ideals tend to feature images of the globe. I examine advertising and images from politically ideological periodicals (e.g., Mother Jones, New Republic, Insight on the News, National Review); political materials from campaigns from the 2004, 2006, and 2008 elections; images and graphics (available on the Internet or through catalogues) on t-shirts and bumper stickers; and logos and other graphics from companies and organizations with political points of view. I speculate about the broader motives and implications of the use of these map images and offer this as a case study for a more general framework of graphical criticism and analysis as it applies to maps and map-like imagery in popular culture.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.012 | 0.016 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".