Laying the Groundwork: Insights from Organisational Ethics for Humanitarian Innovation
Bibliographic record
Abstract
Humanitarian innovation is occurring in a wide range of organisational contexts, from innovation labs and hubs, to specialised units within humanitarian organisations, to small social innovation startups and through intersectoral partnerships. Ethical considerations associated with innovation activities have been the source of increased discussion, including critiques around inclusion in the definition of problems, imposition of solutions, introduction of new risks for people in crisis situations and potential for exploitation. To promote ethical innovation, various initiatives have sought to articulate guiding values and to create resources and frameworks to integrate values in project design and implementation. A distinctive yet complementary line of ethical analysis is offered by the approach of positive organisational ethics, which considers the features of organisations that promote and sustain conditions supportive of ethical action. In this paper we examine three dimensions of an organisation’s ethical infrastructure: the resources that are established, such as policies and statements of organisational values; the practices that are enacted, such as methods of onboarding new staff; and the capacities that are fostered and accessed, including ethics knowledge and skills. Attention to these features constitutes an important means of laying the groundwork for organisational conditions that are supportive of ethical humanitarian innovation.
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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.097 |
| Scholarly communication | 0.022 | 0.016 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".