Middlemen No More? Emergent Patterns in Congressional Leadership Selection
Bibliographic record
Abstract
For a quarter-century, conservative Republicans have used the “San Francisco liberal” label to place Democrats as outside the American mainstream. Imagine their dismay as the 110thCongress opened in January 2007 and Nancy Pelosi, a San Francisco Democrat well to the left of most of her party, ascended to the podium as speaker of the United States House of Representatives. This was, to be sure, a departure. Traditionally, House Democrats had selected ideological “middlemen” for top leadership posts (Truman 1959), particularly those from the “Austin-Boston alliance” that held unbroken sway in House Democratic leadership selection from the initial teaming of Speaker Sam Rayburn of Texas and Majority Leader John McCormack of Boston in 1940 to that of Tip O'Neill of Boston and Texan Jim Wright in the 1970s and 1980s. During this time, Democrats “almost never” selected “‘Americans for Democratic Action-type’ liberals” as leaders (Peabody 1976, 470).
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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 | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".