Toward a model of the municipal evidence-based decision process in the strategic digital city context
Bibliographic record
Abstract
Technological and connected modern cities demand effective decisions by managers that are aligned with citizens demands. The strategic digital city that comprises strategies, information, services, and information technology resources can provide the necessary context so that decisions based on evidence are made possible at the municipal level of management. The objective of this study is to propose a municipal decision process model in the context of the strategic digital city. The research methodology employed was qualitative and applied to circumstantial theoretical reality, emphasizing exploratory and descriptive methods aided by bibliographic and documentary survey along with the non-participatory observation of the variables that make up the model. Similar models were identified and analyzed. The municipal decision process in the context of the strategic digital city was built from three constructs: decision, evidence, and strategic digital city. These constructs are interconnected by their thirteen variables, which are related to the conceptual base of the model developed. The conclusion reinforces the importance of using evidence to support the decision process, making it one of the strategic elements for digital cities aiming at improving their citizens quality of life.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".