An emergent taxonomy of boundary spanning in the smart city context – The case of smart Dublin
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.
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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.013 | 0.033 |
| Scholarly communication | 0.016 | 0.021 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".