What Does ‘Closing Well’ Entail for Humanitarian Project Data? Seven Questions as Humanitarian Health Projects Are (Being) Closed or Handed Over
Bibliographic record
Abstract
Humanitarian health projects generate extensive amounts of data as part of their activities. In many situations, this data will endure long after the projects have ended. Careful attention is needed within project closure planning and implementation to decisions of when and how to share, store, return to the individuals from whom it was collected, or destroy data. Drawing on a review of the literature and guidelines related to data responsibility and project closure, we propose seven questions that can help orient reflection and deliberation around data management from the perspective of an ethics of project closure. The questions foreground considerations related to purpose limitation and data minimisation, respect for data rights, upholding duties of care, clarifying expectations, commitments and agreements, minimisation and mitigation of risk, and alignment of policy and regulatory frameworks for data responsibility. We illustrate the application of the questions to a case study of the handover of a healthcare project in a refugee camp where project activities were transferred from an international humanitarian organisation to local authorities. This analysis reinforces the importance of understanding data responsibility as an essential component of ‘closing well’.
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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 | 0.177 | 0.215 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.020 | 0.085 |
| Scholarly communication | 0.024 | 0.046 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.020 | 0.018 |
| Insufficient payload (model declined to judge) | 0.003 | 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".