Exploring capability and accountability outcomes of open development for the poor and marginalized: An analysis of select literature
Bibliographic record
Abstract
Open development concerns the application of digitally-enabled openness to radically change human capability and governance contexts (Davies & Edwards, 2012; Smith & Reilly, 2013; Smith, Elder, & Emdon, 2011). However, what openness means, and how it contributes to development outcomes is contested (Buskens, 2013; Singh & Gurumurthy, 2013). Furthermore, the potential of open development to support positive social transformation has not yet materialized, particularly for marginalized populations (Bentley & Chib, 2016), partly because relatively little is known regarding how transformation is enacted in the field. Likewise, two promising outcomes – the expansion of human capabilities and accountability – have not been explored in detail. This research interrogates the influence of digitally-enabled openness on transformation processes and outcomes. A purposeful sample of literature was taken to evaluate outcomes and transformation processes according to our theoretical framework, which defines seven cross-cutting dimensions essential to incorporate. We argue that these dimensions explain links between structures, processes and outcomes of open development. These links are essential to understand in the area of Community Informatics as they enable researchers and practitioners to support effective use of openness by and for poor and marginalized communities to pursue their own objectives.
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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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.024 | 0.024 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".