An emergent taxonomy of boundary spanning in the smart city context – The case of smart Dublin
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
Smart cities emphasize the use of advanced technology to deliver better services to and improve the well-being of their residents. Since the administrative authorities that manage cities often lack the knowledge and skills needed to transform their operations in this way, smart city initiatives usually involve a complex set of actors, from local urban authorities and their technical departments to small and large IT firms, academics, and civic organizations, as well as individual citizens. Mediating organizations are often set up to coordinate and manage such interactions. However, little is known about the roles and activities of such bodies. Using data from the Dublin smart city projects, this study draws on the concept of boundary spanning to develop a taxonomy of the work of such intermediaries. Divided into technical, political, social, and cultural domains, the study demonstrates the critical role of the work done by such bodies in enhancing collaboration among and the participation of a diverse group of citizens, IT and digital strategy departments of local authorities, universities and local/international IT companies (e.g., Google, Facebook or Airbnb), leading to a bottom-up governance style of leading smart city initiatives and projects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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