Intermediation in Open Development: A Knowledge Stewardship Approach
Bibliographic record
Abstract
Open Development (OD) is a subset of ICT4D that studies the potential of IT-enabled openness to support social change among poor or marginalized populations. Early OD work examined the potential of IT-enabled openness to decentralize power and enable public engagement by disintermediating knowledge production and dissemination. However, in practice, intermediaries have emerged to facilitate open data and related knowledge production activities in development processes. We identify five models of intermediation in OD work: decentralized, arterial, ecosystem, bridging, and communities of practice and examine the implications of each for stewardship of open processes. We conclude that studying OD through these five forms of intermediation is a productive way of understanding whether and how different patterns of knowledge stewardship influence development outcomes. We also offer suggestions for future research that can improve our understanding of how to sustain openness, facilitate public engagement, and ensure that intermediation contributes to open development.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".