Sustainable urban digital innovation: A socio-technical competency-based approach to evaluation
Bibliographic record
Abstract
This study explores the leadership competencies required in practice by city planners and managers in smart city projects focusing on environmental urban sustainability . Although the literature notes that urban technologies and their capabilities can help address sustainability challenges in cities, there is a lack of studies exploring the competency requirements necessary to foster leadership capacity. This paper identifies leadership competencies within four real-world case studies in the urban built environment, guided by a socio-technical competency framework (DC2-CF). The selected case studies represent a diverse set of city planning purposes, geographic regions, various levels of spatial scale, and socio-technical elements of digital innovation . In these case studies, city managers exhibit specific competencies to develop digital innovation projects that uphold and advance urban sustainability. The study demonstrates the relevance and practical application of DC2-CF as a valuable tool to identify competency needs for local public, private, and community stakeholders throughout diverse stages of the urban digital innovation process. The findings suggest the complex relationship between competencies and project delivery, stressing variations in how they are utilised across various projects. Drawing from these key results, this paper provides practical recommendations for city professionals, guiding them in leading climate-friendly and sustainable urban digital innovation.
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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 | 0.067 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".