Researching the emerging impacts of open data: revisiting the ODDC conceptual framework
Bibliographic record
Abstract
Open data has rapidly moved from being a niche interest, to being part of the global policy mainstream. Government-led open data initiatives have spread across the globe, and civil society or technologist experiments using data to improve governance have been spreading organically, from budget monitoring in Nigeria, to court transparency projects in Argentina. It is increasingly seen as enabler of a “data revolution” in the process of decision-making and accountability. However, understanding how experience of open data will vary from country to country and context to context, and, understanding the common features of open data that are shaping its implementation in these diverse settings, requires broad-based research framework. It requires research that can engage with both existing realities of decision-making in sectors, acknowledging the growing complexity of this process in an increasingly networked society. In this paper we have reviewed the framework of the “Open Data in Developing Countries”(ODDC) project, the largest research project on the impact of open data in developing countries to date. The framework was designed to help explore the link between openness in the data ecosystem, decentralized changes in decision-making, and positive and negative emerging impacts such as transparency and accountability, inclusion and empowerment as well as innovation and economic development. It was tested to generate cross-learning from 17 in-depth cases studies in 14 countries, as well as generate policy-relevant findings. This paper reviews and updates the original framework based on the findings and reflections developed during the research project.
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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 | 0.069 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.011 | 0.113 |
| Scholarly communication | 0.029 | 0.042 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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".