Who Are Government OpenData Infomediaries? A Preliminary Scan and Classification of Open Data Users and Products
Bibliographic record
Abstract
Open data, that is, the provision of government data in a publicly accessible, machine-readable format, with liberal usage terms, has become commonplace. Despite the promise of open data, there are many questions about who is accessing government open data and what they are using it for. This research presents a characterization of the infomediary, a third party who accesses government open data and creates value-added products from it. Using four major Canadian municipal open data providers, we present an information scan and classification of open data infomediaries and the products they create. Five classifications of infomediary are proposed: government, private sector, NGO, academic, and media. Within each of these classifications, the type of infomediary products created and the delivery method used are summarized. Findings from this research indicate a diversity in infomediary actors and products, but that this activity is largely concentrated in government and private sector infomediary types. Further considerations of the impact of infomediary activity on government open data provision are presented as important future directions of research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".