Bibliographic record
Abstract
Foreword Kofi Annan Preface S.Hidalgo Executive Summary The Humanitarian Response Index 2007 L.Altinger, S.Hidalgo, and A.Lopez-Claros The Birth of Good Humanitarian Donorship J.Schaar Progress on the Front Lines J. Egeland Opening Space for Long-Term Development in Fragile Environments S. Cliffe and C. Petrie The Media- Driven Humanitarian Response: Public Perceptions and Humanitarian Realities as Two Faces of the Same Coin M. Ogrizek Colombia: A Crisis Concealed S. Hidalgo Democratic Republic of the Congo: Sick Giant of Africa- G. Gasser Haiti: Violence, Ganga, and a Fragile State on the Brink of Crisis- R. Sole Lebanon: Crisis of Civilian Protection Niger: Crisis of acute, Protracted Poverty and Vulnerability- M. Maranon Pakistan: Testing Reform of the Humanitarian System- R. Polastro Sudan: From One Crisis to Another Timor-Leste: Relapse and Open wounds- S. Hidalgo Summary: 2006- A Year of Emergencies Donor profile: Australia Donor profile: Austria Donor profile: Belgium Donor profile: Canada Donor profile: Denmark Donor profile: European commission Donor profile: Finland Donor profile: France Donor profile: Germany Donor profile: Greece Donor profile: Ireland Donor profile: Italy Donor profile: Japan Donor profile: Luxembourg Donor profile: Netherlands Donor profile: New Zealand Donor profile: Norway Donor profile: Portugal Donor profile: Spain Donor profile: Sweden Donor profile: Switzerland Donor profile: United Kingdom Donor profile: United States
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 | 0.027 | 0.198 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.047 | 0.013 |
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".