EXAMINING PRESIDENTIAL IMMUNITY: THE JANUARY 6 RIOT, TRUMP, AND THE 14TH AMENDMENT - AN IMPERATIVE CONSIDERATION OF CONSTITUTIONAL LAW
Bibliographic record
Abstract
The work critically engulfs the events surrounding the January 6 riot, interactions, Trump’s alleged involvement, and the application of the 14th Amendment’s Section 3. The central question of the study puzzles on whether the said Amendment grants States jurisprudence to determine or alter presidential immunity. Drawing on Montesquieu's theory of separation of powers - a natural pillar of Federalism that was fundamental in drafting the US Constitution, the work embarks on an exploration that investigates the intersections of States prerogatives on Federal institutions. Building on these constructions, the work delves into intricate legal debates interlocking constitutional interpretation, and emphasizing its potential ramifications. The study employs comparative methodology; incorporating the ten Southern-States that barred Abraham Lincoln, and the case of USA vs Nixon. The approach illuminates political materialism, an enduring theme in American politics, thus, producing a better understanding of the state of the art, through the intrinsic corollaries of federalism in the ever evolving extensive milieus of political constitutionalism and legal canons.
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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.040 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".