Conceptual Model for Assessing Logistics Maturity in Smart City Dimensions
Bibliographic record
Abstract
The advancement of new technologies and the increasingly inseparable presence of logistics systems in the daily life of cities, industries, companies, and society has been modifying how logistics processes are implemented in these environments based on technological innovations, internet, virtual businesses, mobility, and the use of multi-channel distribution. Together with these changes, urban centers have been connecting to the smart city concept as the understanding of this theme advances into the debate and improvements in the agendas of either public or private management. This research proposes a conceptual model for evaluating logistics maturity in the smart city dimensions. The method has a qualitative, exploratory, and descriptive approach, supported by the Delphi method, which uses a questionnaire and interview as a data collection instrument with specialists on the subject. We identified that qualifying logistics in the urban environment is complex and requires a specialized look at identifying cities’ structural, geographic, regional, social, and environmental characteristics. As a social–technological contribution, the proposition of the logistics maturity assessment scale in smart city dimensions can serve as an evaluative model of logistics, which means helping in urban planning and strategic management of cities, offering smarter solutions to the realities of urban spaces.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".