Internet of Things : The New Government-to-Business Platform
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The buzz around Internet of Things (IoT) \n has gathered momentum but the IoT phenomenon is poorly \n understood by governments and businesses. Governments are \n under pressure to become more innovative, evidence-based, \n and collaborative and IoT seems to offer opportunities such \n as increased competitiveness and innovation, and regulatory \n improvements that reduce the burden on business and increase \n compliance. In this report we examine the evidence on the \n ground to see how the theoretical potential of IoT \n implementation matches up with the reality on the ground and \n what can we learn from government agencies at the forefront \n of IoT implementation. The report draws on lessons from \n cities around the world (Germany, UK, Luxembourg, Estonia, \n Kazakhstan, Finland, Canada, USA, Japan, UAE, and India); it \n also provides a review of the IoT marketplace. The questions \n it answers include - what is IoT and why should governments \n care, how are different cities implementing IoT based \n solutions, and what are the main policy and other \n implications for government to fully utilize the potential \n of the technology while managing the associated risks and \n challenges? Findings include the fact that IoT \n implementation is still nascent in governments, the business \n models to scale pilots are still under-developed, the policy \n environment remains very patchy, and there is need to invest \n in digital capacity, data practices, and IoT infrastructure. \n The report includes a rough toolkit for government agencies.
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.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.007 |
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 it