Situating Open Data
Bibliographic record
Abstract
Open data and its effects on society are always woven into infrastructural legacies, social relations, and the political economy. This raises questions about how our understanding and engagement with open data shifts when we focus on its situated use. To shed a light on these questions, Situating Open Data provides several empirical accounts of open data practices, the local implementation of global initiatives, and the development of new open data ecosystems. Drawing on case studies in different countries and contexts, the chapters demonstrate the practices and actors involved in open government data initiatives unfolding within different socio-political settings. The book proposes three recommendations for researchers, policy-makers and practitioners. First, beyond upskilling through data literacy programmes, open data initiatives should be specified through the kinds of data practices and effects they generate. Second, global visions of open data implementation require more studies of the resonances and tensions created in localised initiatives. And third, research into open data ecosystems requires more attention to the histories and legacies of information infrastructures and how these shape who benefits from open data flows. As such, this volume departs from the framing of data as a resource to be deployed. Instead, it proposes a prism of different data practices in different contexts through which to study the social relations, capacities, infrastructural histories and power structures affecting open data initiatives. It is hoped that the contributions collected in Situating Open Data will spark critical reflection about the way open data is locally practiced and implemented. The contributions should be of interest to open data researchers, advocates, and those in or advising government administrations designing and rolling out effective open data initiatives.
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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 | 0.019 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.011 | 0.026 |
| Scholarly communication | 0.024 | 0.031 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".