A Multidimensional Information Management Framework for Strategic Digital Cities: A Comparative Analysis of Canada and Brazil
Bibliographic record
Abstract
Abstract Management and information systems are essential for strategic cities since they provide customized digital services that connect specific information and its context to form a multidimensional construct. The objective of this study is to perform an information analysis in two cities to develop a strategic multidimensional framework. The research methodology was based on the model theory. It took into consideration the digital services from two cities supported by non-participatory observations and a bibliographic review. The data were collected hierarchically and compared with five related international frameworks using the infomapping technique. The framework comprised three constructs and ten multidimensional variables that related the conceptual theories to the developed and applied model. The research was conducted in Rio de Janeiro, Brazil, and Regina, Saskatchewan, Canada. The results indicated disconnections between one or more of the variables surveyed, limited customized services, and recurrent use of information in a bidirectional form. The conclusions emphasized the multidimensional character of information in terms of its dynamic nature and relations with distinct levels of information management. In addition, the study established a framework for strategic digital cities based on new interactive relations between public information management and digital services, including the city’s strategic policies. In terms of its contribution to the literature, this research highlighted the dynamic nature of information and strategic digital cities.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.021 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".