Making a City Smarter through Information Integration: Angel Network and the Role of Political Leadership
Bibliographic record
Abstract
Scholars and practitioners around the world are increasingly using terms such as smart cities and smart governments. The essence of becoming smarter seems to be related to connectedness, responsiveness, efficiency, and sustainability. Therefore, by integrating their most important information and services, cities can achieve some of the goals and objectives extensively identified with smartness. This paper aims to show that despite important challenges, information integration initiatives can be implemented with relatively good results if there is enough political support from top government executives. We conducted semi-structured interviews with government managers responsible for the Angel Network (AN) system, which attempts to integrate key information about social programs in Mexico City. This work offers insights on how the support of the mayor can significantly influence the implementation of an information integration strategy in at least three different ways: (1) the creation of an adequate institutional framework, (2) the alignment of diverse political interests within the city administration, and (3) the increase of financial resources.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".