Toolkit: Ethical human-centred design for a complex world
Bibliographic record
Abstract
How might we leverage a system of tools to help development professionals customise and operationalise ethical, impact-oriented human-centred design work? The Accelerate Impact team, working closely with an Aga Khan Foundation initiative called Local Impact, has partnered with USAID to develop a robust and user-friendly innovation toolkit consisting of a set of human-centred design (HCD) guidebooks and additional tools to support teams to move through the HCD process. This interactive session spans the pre-work of scoping and resourcing the project—to the engagement of the community in identifying problems and creating solutions—all the way to planning for implementation and scale, pitching the idea to potential partners and funders and assessing the impact of the solutions over time. The resulting resources are a systematic and cohesive set of tools and a process for customising those tools to suit the needs of a particular project. We hope this encourages more social sector folks to leverage HCD to engage the community in ethical ways while developing feasible, viable and desirable (innovative) solutions. As we prepare to launch these resources—free and open source to the global social impact community (and beyond!)—we humbly ask for this community’s engagement and critical feedback to help us to refine these tools and make them the most rigorous, thoughtful and user-friendly as they can be.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".