Cartographic Propaganda in the Age of Social Media: Empirical Evidence from Ethiopia
Bibliographic record
Abstract
Cartographic propaganda is a conscious manipulation of a map to influence the reader’s belief. Countries often use it to claim disputed territories or project fear over opposing nations or political alliances, but little is known about the manipulations of maps along internal sociolinguistic and political fault lines on social media platforms. The author investigated the nature and intent of propaganda maps in Ethiopia before and after the 2018 government reform based on six purposely sampled maps prominently circulated on social media. While falling short of the acceptable cartographic qualities, the analysis of sample propaganda maps revealed two fundamental characteristics during the pre- and post-government reform. First, their role shifted from a centripetal force in the political coalition to a centrifugal force in the coalition’s disintegration. Second, their mode of dissemination transitioned from cartographic misinformation to disinformation. The findings of this study contribute empirical evidence to the ongoing cartographic information discourse that lags behind the rapidly changing map-making and map-sharing platforms in the age of geospatial and social media revolutions.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".