Post-War Iraq: Foreign Contributions to Training, Peacekeeping, and Reconstruction
Bibliographic record
Abstract
Securing and maintaining foreign contributions to the reconstruction and stabilization of Iraq has been a major priority for U.S. policymakers since the launch of Operation Iraqi Freedom in March 2003.This report tracks important changes in financial and personnel pledges from foreign governments since the August 19, 2003 bombing of the U.N. Headquarters in Baghdad and major events since the fall of Baghdad on April 9, 2003.Currently, there are 26 countries with military forces participating in the coalition's stabilization effort.An additional 14 countries have withdrawn their troops from Iraq due to either the successful completion of their missions, domestic political pressure to withdraw their troops, or, in the case of the Philippines, the demands of terrorist kidnappers who threatened to kill foreign hostages unless their respective countries removed their troops from Iraq.Most foreign pledges for reconstructing Iraq were made at a donors' conference in Madrid, Spain, in October 2003.Foreign donors pledged an estimated $13 billion in grants and loans for Iraq reconstruction, but have only disbursed about $3 billion to the United Nations and World Bank trust funds for Iraq.The largest non-American pledges of grants have come from Japan, the United Kingdom, Canada, South Korea, and the United Arab Emirates.The World Bank, International Monetary Fund, Japan, and Saudi Arabia have pledged the most loans and export credits.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".