Responding to the Challenges of the Millennium Development Goals - World Bank Support for Land Administration”, Geoconference Quebec 2007
Bibliographic record
Abstract
Land and property are generally the major assets in any economy. In most countries, land may account for between half to three-quarters of national wealth. Land is a fundamental factor for agricultural production and is thus directly linked to food security. Security of land tenure is an important foundation for economic development, social and environmental management, and also for supporting reconstruction following a disaster or conflict. There are many complexities, dimensions and themes associated with land administration and management. Securing land rights is particularly relevant to vulnerable groups such as the poor, women, orphans, displaced persons and ethnic minority groups. Fees and taxes on land are often a significant source of government revenue, particularly at the local level. In most societies, there are many competing demands on land including development, agriculture, pasture, forestry, industry, infrastructure, urbanization, biodiversity, customary rights, ecological and environmental protection. Many countries have great difficulty in balancing the needs of these competing demands. Land continues to be a cause of social, ethnic, cultural and religious conflict. For many centuries, many wars and revolutions have been fought over rights to land. Throughout history, virtually all civilizations have devoted considerable efforts to defining
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.061 | 0.008 |
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".