History of Trading Currencies in the Upper Cross River Region of Nigeria Before the Nineteenth Century
Bibliographic record
Abstract
The history of the Upper Cross River Region of Nigeria has suffered neglect in the area of scholarly interest for a very long time. Until recently, the area was one of the least known in Nigeria. Early European mariners to the region cast aspersions on the culture history of the people and labelled them indiscriminately as “fragments of earlier world”, “human clusters”, and “splinter zone”. Given this lacuna in the culture history of the Upper Cross River Region, this paper is a bold attempt at documenting and articulating some coherent perspectives of the culture history of the people. Using sources in its methodology, the paper highlights the level of sophistication of the economy which existed in the area prior to the nineteenth century. It also serves to situate the history of the region in its proper context, showing that far from being a mere subsistence economy which was dormant, rigid and unprogressive, it was flexible and basically dynamic. The research concludes that contrary to the misconception of visitors to the region, the people had developed a viable and vibrant economic system which utilised a variety of currencies in exchange transactions. This also depicts the contributions made by ancestors of the people towards development in the region long before the coming of Europeans.
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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 | 0.001 |
| 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 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".