Bibliographic record
Abstract
The policy of the J. Biden administration to return to normality in the functioning of the US political system is analyzed. Political normality is interpreted as an attempt to "finally reverse" the period of the Republican administration of Donald Trump in power. The return of American politics to normal tracks of political processes has come across a well-known pattern that has been in effect throughout the entire period of modern and recent US history, in which every newly elected president and his party lose a significant number of seats in the House of Representatives in the next midterm elections. and often in the Senate, leading to the loss of control over one or even two houses of the US Congress. At present, it can be assumed that this pattern will also manifest itself in the 2022 midterm elections, which will definitely become a “referendum on J, Biden.” In the 2022 elections, the decisive factor for their outcome will be the J. Biden's job approval rating as president, which has fallen below 50% since the end of last summer and has been steadily declining since then. Based on statistical models of the relationship between the job approval rating of the incumbent President and the number of possible flipped seats in the House of Representatives, it is defined as being in the range of 30-40 seats. The loss of control by the Democrats of the US Senate is also not ruled out.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.012 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 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".