The need for standardised methods of data collection, sharing of data and agency coordination in humanitarian settings
Bibliographic record
Abstract
Humanitarian crises and emergencies are prevalent all over the world. With a surge in crises in the last decade, humanitarian agencies have increased their presence in these areas. Initiatives such as the Sphere Project and the Minimum Initial Service Package known as MISP were formed to set standards and priorities for humanitarian assistance agencies. MISP was initiated to coordinate and standardise data and collection methods and involve locals for programme sustainability. Developing policies and programmes based on available data in humanitarian crises is necessary to make evidence-based decisions. Data sharing between humanitarian agencies increases the effectiveness of rapid responses and limits duplication of services and research. In addition, standardising data collection methods helps alleviate the risk of inaccurate information and allows for comparison and estimates among different settings. Big data is a new collection method that can help assemble timely data if resources are available and turn the data into information. Further research on setting priority indicators for humanitarian situations can help guide agencies to collect quality 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 | 0.192 | 0.292 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.014 | 0.022 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".