Inclusive innovation and the “ordinary” city: Incidental or integral?
Bibliographic record
Abstract
Economic opportunity in the 21st century privileges people and places with the “right mix” of human capital to develop and apply digital technologies, and disadvantages those without. Increasing socio-technical, socio-economic, and socio-spatial polarization underscores inclusion as a critically important dimension of innovation. Workforce development and entrepreneurial ecosystems each have implications for “inclusive innovation” in restructuring cities, but understanding their realistic prospects requires attention to local institutional capacity as well as the broader multilevel policy contexts in which they operate. This study compares inclusive innovation programs in Saint-Etienne, France and Greensboro North Carolina, two mid-sized restructuring cities operating in two different macro-institutional settings. Highly variable but not entirely idiosyncratic dynamics emerge in each case; inclusive innovation is integral in Saint Etienne and incidental in Greensboro but not a resounding success in either city. “Top-down” and “bottom-up” dynamics interact in different ways to shape outcomes, but power matters most for local policy choice. The decisive factor is explicit linkage to a commonly accepted urban development agenda that is supported politically by local government. Intermediary organizations lack influence, and the private sector is disengaged. These findings suggest the need to rethink assumptions about the actor configurations that determine urban development priorities.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.032 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".