Open Data Intermediaries in Developing Countries
Bibliographic record
Abstract
This paper offers a more socially nuanced approach to open data intermediaries using the theoretical framework of Bourdieu’s social model, particularly his species of capital. Secondary data on intermediaries from Emerging Impacts of Open Data in Developing Countries research was analysed according to a working definition of an open data intermediary presented in this paper, and with a focus on how intermediaries are able to linking agents in an open data supply chain, including to grassroots communities. The study found that open data supply chains may comprise multiple intermediaries and that multiple forms of capital may be required to connect the supply and use of open data. The effectiveness of intermediaries can be attributed to their proximity to data suppliers or users, and proximity can be expressed as a function of the type of capital that an intermediary possesses. However, because no single intermediary necessarily has all the capital available to link effectively to all sources of power in a field, multiple intermediaries with complementary configurations of capital are more likely to connect between power nexuses. This study concludes that consideration needs to be given to the presence of multiple intermediaries in an open data ecosystem, each of whom may possess different forms of capital to enable the use of open data.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".