Bibliographic record
Abstract
Over the course of 23 years, United States Senator Susan Collins (R-ME) has been able to successfully walk a unique line of nonpartisanship, never stepping too far to the right, or to the left. However, following her vote to confirm Justice Brett Kavanaugh to the United States Supreme Court in 2017, and her vote to acquit President Trump of his impeachment charges in early 2020, Susan Collins placed herself in an incredibly precarious situation. Pundits and analysts were convinced that this election would turn into a referendum on Susan Collins (Lyall 2020). Meanwhile, her opponent, the current Speaker of the Maine House of Representatives, Sara Gideon, consistently led in the polls and worked off of the momentum gained from the success of the U.S. House Democrats in the 2018 midterms. And yet, Susan Collins stunned the nation by defeating Gideon. This paper evaluates and analyses what possible causes led to this outcome. Ultimately, Collins’ choice to vote against the confirmation of late-Ruth Bader Ginsburg's replacement on the Supreme Court convinced Mainers that Susan Collins could still be trusted, and should be given another chance.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.052 | 0.010 |
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".