Burden and Standard of Proof in Election Petitions without Criminal Allegations
Bibliographic record
Abstract
The burden and standard of proof in election petition without criminal allegation is in tandem with the extant Evidence Act, as election petitions is sui generis. The purpose of election laws is to obtain a correct expression of the intent of the voters. However, this paper argues that whereas proof of election petition without criminal allegations requires proof on the preponderance of evidence, the shallow chant of “he who asserts must prove” in the extant law is a conduit pipe for electoral injustice. This paper therefore makes a clarion call for the amendment of the relevant extant law to usher in a legal regime of burden of proof on the pleadings, where whoever asserts the affirmative or positive must prove on the state of the pleadings. The rebuttable presumption of the regularity of the conduct of elections and declaration of results, no longer serve the end of justice in our electoral process. This paper therefore argues that the Electoral Act be amended to place the burden of proof of the regularity of elections and declaration of results on the Independent National Electoral Commission (INEC), to meet the desired justice contemplated in the electoral process.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Legal argument about burden and standard of proof in election petitions; 'evidence' and 'proof' here are legal, not research, concepts.
The paper analyzes legal standards of proof in election petitions.
Legal analysis of burden of proof in election petitions; electoral law, not research practice.
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 | 0.099 | 0.297 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.008 | 0.040 |
| Scholarly communication | 0.023 | 0.028 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.017 | 0.018 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".