The history and sources of conflict of laws in Nigeria, with comparisons to Canada
Bibliographic record
Abstract
I had often wondered how, about five hundred or more years ago, cases with foreign elements were resolved in the geographical territory that is now known as Nigeria. My curiosity invited me to this intellectual pursuit with its unanticipated profound and challenging dimensions. From Nigeria I had to grapple equally with a comparable legal situation in Canada, and to proffer explanations for both colonial and post-colonial legal regimes. Mine has been only a flirtation with Canadian legal history! This work has shown some eclecticism, borrowing from history, jurisprudence, constitutional law, and conflict of laws. Professor D. J. Guth ensured that I did not get stuck in the quagmire of historical materials or confused in their exposition. Chapter 1 introduces the reader to conflict of laws by giving its definition and principal characteristics. Chapter 2 explains the historical context for the evolution of conflict of laws in Nigeria. Chapters 3, 4, and 5 treat the various sources of conflict of laws in Nigeria and Canada. Chapter 6 focuses on the special problems of conflict of laws in Nigeria engendered by its pluralistic legal system. Each chapter tries to state what seems to be the legal position and then makes suggestions on what the legal position ought to be.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.026 | 0.008 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".