The Reality of Aid 1997-1998: An independent review of development cooperation
Bibliographic record
Abstract
List of Boxes and Tables The Reality of Aid Project Acknowledgements ICVA and Eurostep Member Organisations Participating Agencies Part I Current Issues and Key Themes Introduction Development Cooperation in a Changing World Part II OECD Country Profiles Australia Austria Belgium Canada Denmark Finland France Germany Ireland Italy Japan Netherlands New Zealand Norway Portugal Spain Sweden Switzerland United Kingdom United States European Union The EU and Africa Part Ill Perspectives from the South International Cooperation in Argentina Western Assistance to Post-communist Countries in Central and Eastern Europe A comment on NGOs, Ownership and Participation in Ghana Guatemala Haiti Breaking New Ground in Donor Coordination in India International Cooperation with Latin America Internal Management in Relation to Uganda's External Debt Gender Equity in Education in Zimbabwe Part IV Aid Trends, Facts and Figures World Aid at a Glance World Aid in 1995 and 1996 Trends in Aid and Development Cooperation The Outlook for Aid Leadership on Public and Political Opinion Spending on Public Information and Development Education Measuring and Mainstreaming Aid for Poverty Eradication Approaches to Gender in Development Cooperation Humanitarian Relief, Conflict and Emergencies Trends in ODA through NGOs Political Responsibility and Management of Development Cooperation Glossary Exchange Rates Notes on Data
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.015 | 0.062 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".