Viscous Open Data: The Roles of Intermediaries in an Open Data Ecosystem
Bibliographic record
Abstract
Open data have the potential to improve the governance of universities as public institutions. In addition, open data are likely to increase the quality, efficacy and efficiency of the research and analysis of higher education systems by providing a shared empirical base for critical interrogation and reinterpretation. Drawing on research conducted by the Emerging Impacts of Open Data in Developing Countries project, and using an ecosystems approach, this research paper considers the supply, demand and use of open data as well as the roles of intermediaries in the governance of South African public higher education. It shows that government's higher education database is a closed and isolated data source in the data ecosystem; and that the open data that are made available by government is inaccessible and rarely used. In contrast, government data made available by data intermediaries in the ecosystem are being used by key stakeholders. Intermediaries are found to play several important roles in the ecosystem: (i) they increase the accessibility and utility of data; (ii) they may assume the role of a “keystone species” in a data ecosystem; and (iii) they have the potential to democratize the impacts and use of open data. The article concludes that despite poor data provision by government, the public university governance open data ecosystem has evolved because intermediaries in the ecosystem have reduced the viscosity of government data. Further increasing the fluidity of government open data will improve access and ensure the sustainability of open data supply in the ecosystem.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Open science Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | Open science Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
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 | 0.023 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.010 | 0.028 |
| Scholarly communication | 0.037 | 0.060 |
| Open science | 0.002 | 0.024 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".