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Record W4312230541 · doi:10.31857/s2686673022040010

J. Biden Administration: Thorns and Roses of Return to “Normality”

2022· article· en· W4312230541 on OpenAlexaff
Natalya Travkina

Bibliographic record

VenueUSA & Canada Economics – Politics – Culture · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicEconomic and Technological Developments in Russia
Canadian institutionsInstitute for Christian Studies
Fundersnot available
KeywordsReferendumPoliticsHouse of RepresentativesAdministration (probate law)NormalityPower (physics)Political sciencePolitical economyLawEconomicsPsychologySocial psychology

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.948
Threshold uncertainty score0.785

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.017
GPT teacher head0.258
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2022
Admission routes1
Has abstractyes

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