Bibliographic record
Abstract
Since achieving political independence from Great Britain in 1960, Nigeria has assumed full responsibility for the mapping of her territories. Fifty years on, the country has made some progress in mapping; this progress has not met all mapping needs, but it has advanced into the realm of the geospatial sciences. Digital systems linked to satellite observations and aerial surveys have been developed. There remains a gap between the demand for and the supply of maps and allied products in Nigeria. In the early part of the period under study (1960–2010), many mapping projects initiated in Nigeria became moribund, while some did not get off the ground at all. Consequently, like most African countries, Nigeria still lacks basic maps needed for judicious resource planning, development, and management. Instability coupled with lack of a well-established mapping policy, shortage of funding, and successive governments' failure to integrate mapping into national development has affected mapping in the country. The national development process has thus far not fostered the growth of a rich mapping culture. Nigeria, like many other countries in Africa, needs a national geospatial data infrastructure (NGDI), a robust mapping policy, improved funding of mapping programs, and strong cartographic institutions. If vigorously pursued, those measures will surely revolutionize the state of mapping in Nigeria and the rest of Africa.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| 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".