Bibliographic record
Abstract
The current assessment of the state of political division in the United States is foreboding. Americans are more divided than any time since the Civil War, leaving some to opine that these differences may be irreconcilable. This speculative analysis takes seriously as its point of departure the position of a growing number of American commentators and policy experts who argue that the United States exhibits many of the risk factors that could lead down the path toward another civil war. Some of these commentators have advocated breaking up the union to pre-empt this outcome. The critical analysis within this article draws upon historical analogues from states partitioned during the 20th century such as such as the Soviet Union, Yugoslavia, Czechoslovakia, Palestine, and India. These comparisons are used to evaluate proposals for a geographical sundering of the United States into Red and Blue Americas. My analysis highlights the ways in which any kind of national dissolution, though appealing to some at first glance, would be more politically complex, demographically fraught, and possibly no-less violent than the alternative of civil conflict. The most promising alternative appears to be that of learning to live and work together through difference.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.002 |
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".