Commissioning Development: Grantmaking, Community Voices, and their Implications for ICTD
Bibliographic record
Abstract
While information and communication technology for development (ICTD) researchers have prioritized advocating for community voices in innovation design and development, we have limited insights into how community voices are incorporated by the high-level decision-makers who fund and initiate development projects and programs in the Global South. Indeed, understanding local communities’ voices (expressions of needs, challenges, and priorities) in tailoring effective development projects for sustainable development is widely considered an unmet goal. Using a qualitative survey of eight decision-makers (including grantmaking donors, central governments and INGOs) we explored a number of key factors, including national and global political climates, insider-outsider interactions, and evidence-based approaches that influence the high-level decision making process, workflows, and perceptions of community voice in project commissioning within Bangladesh’s public health nutrition development arena. Our findings reveal the tensions that arise among high-level decision-makers, and highlight the challenges associated with connecting with communities during development project design and implementation. We suggest broader implications and design opportunities for inventive project commissioning approaches to bridge the gap between communities and decision-makers. Our findings are of potential value for ICTD and HCI4D researchers interested in sustainable innovation and understanding and participating in the complex workflows of the project commissioning process in sustainable global development.
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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 | 0.098 | 0.190 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.021 | 0.039 |
| Scholarly communication | 0.029 | 0.022 |
| Open science | 0.003 | 0.027 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".