Bibliographic record
Abstract
Drawing upon the smart experiences of "world class" cities in n.America, Canada and Europe, this book provides the evidence to show how entrepreneurship-based and market dependent representations of knowledge production are now being replaced by a community of policy makers, academic leaders, corporate strategists and growth management alliances with the potential to liberate cities from the stagnation they have previously been locked into. In particular, it will show how such market-based representations of knowledge production are now being replaced by a community of policy makers and academic leaders with: • the corporate strategies and growth management alliances capable of reaching beyond the idea of "creative slack";• a notion of innovation which allows cities to reach beyond the idea of creative slack and be "smarter" in generating the intellectual capital that is needed to meet the efficiency requirements of wealth creation;• the "wealth of intellect' also needed for cities to not only be economically innovative and culturally creative, but smarter in drawing upon the enterprise required for industry and government to open-up, reflexively absorb and discursively shape these strategic alliances; • the "creative" means for such a participatory system of governance to be smarter when not only liberating cities from the stagnation they have previously been locked into, but in freeing them up to promote the type of civic renewalsmarter cities also pave the way for.Bringing together the critical insights of papers drawn from a collection of leading international experts on the transition to smart cities, this book proposes to do what has recently been asked of those responsible for Creating Smart-er Cities. That is: to assemble the definitional components, critical insights and institutional means by which to get beyond the all too often self-congratulatory tone cities strike when claiming to be smarter.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.028 | 0.006 |
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".