Electoral geography: From mapping votes to representing power
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract In some ways, electoral geography has never been more popular. From the detailed, online maps of the Brexit vote to discussions of the electoral college versus the popular vote in the 2016 Trump–Clinton U.S. presidential contest, the relationships among geography, voting, and political power have seldom been more visible. The popularity of electoral geography in social and news media, however, does not necessarily reflect its presence in scholarly discussions, and indeed, in some ways, the former has replaced the latter. Digital technology and the burgeoning availability of electronic data mean that it is easier than ever to create maps of votes, often in near real time. Yet the academic field of electoral geography encompasses more than just mapping votes, including the study of election campaigns, political parties, electoral systems, and gerrymandering. The 3 major approaches are the geography of voting (mapping and visualizing votes), geographic influences on voting (the effect of place on political preferences and behavior), and the geography of representation (the analysis of electoral systems). Indeed, the structure of the electoral system, including gerrymandering, is often the key to understanding how political and racial/ethnic minorities can (or cannot) wield power and influence. This article examines each approach after a brief review of the historical origins of the subfield.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 it