Disintermediating Government: The role of Open Data and Smart Infrastructure
Bibliographic record
Abstract
Governments are increasingly negotiating the adoption of civic technologies to improve government functioning and to better connect with citizens. Despite the benefits of civic technology to make government more efficient, effective, and transparent, there are many challenges and even unintended outcomes to civic technology adoption. This exploratory paper presents a conceptual argument using two types of civic technology; open data and smart city infrastructure, as examples where their procurement by government can disintermediate government from citizen. This disintermediation can have both positive and negative outcomes for different parties. Four mechanisms that drive this disintermediation are discussed, including the use of legal frameworks, jumping of scales, conversion of public to private goods, and the creation of standards. These mechanisms can serve to shift the role of government from a service provider to a more background role as a data custodian or regulator, opening many opportunities for other actors, including private sector to assume critical roles in service provision.
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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 | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.061 |
| Scholarly communication | 0.021 | 0.033 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".