The Concept of Automatic Disqualification or Mandatory Recusal by Judges with Interest in Matters Before them: The Unsettling and Its Impact on Judicial Corruption in Nigeria
Bibliographic record
Abstract
This treatise critically examines the Concept of Mandatory Recusal by Judges with Interest in Matters before them and its Impact on Judicial Corruption in Nigeria. Lord Chief Justice Hobart's statement in Day v Savadge emphasized that statutes against natural equity, such as making a man judge in his own case, are inherently void. The theory of automatic disqualification, originating from Dimes v Grand Junction Canal, has become a tool for judges in Nigeria to manipulate justice and sideline political opponents. The unsettling trend of automatic disqualification based on bias is becoming prevalent, raising concerns about judicial integrity. Recent statements and actions by public officials, including Senator Adamu Bulkachuwa, suggest that the judiciary is influenced by political interests. The Nigerian Bar Association condemned Senator Bulkachuwa's admissions but no action has been taken, leaving the public skeptical about the judiciary's impartiality. This study argues for a strict and universally accepted application of the recusal doctrine to strengthen jurisprudence. Judges with any potential bias should recuse themselves to ensure justice is not only done but also seen to be done. While some legal scholars argue for practical reasons to abolish automatic disqualification, the need for judicial impartiality remains paramount. The Canadian Judicial Council emphasizes that judges, despite their experiences and opinions, must have an open mind and be free to consider different viewpoints. The work cites cases like R v Bow Street Magistrate; Ex parte Pinochet (No 2) to support the concept of automatic disqualification. It concludes that to combat corruption in the judiciary and restore public confidence, there must be collective agreement on the necessity of automatic disqualification for any interests, regardless of their magnitude. Upholding the principle of recusal is essential, as when a case is on trial, the judge's integrity is also at stake.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".