Sustainable urban digital innovation: A socio-technical competency-based approach to evaluation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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. • Essential socio-technical competency requirements for sustainable digital innovation. • Exemplary case studies to illustrate sustainable and digital innovation leadership. • Practical application of the DC2-CF as a valuable tool to identify competency needs. • Valuable insights revealing how to lead sustainable digital innovation responsibly. • Practical recommendations to align urban digital innovation with sustainable goals.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it