Small States and Small Cities: Using Interpersonal Networks to Accelerate Economic Restructuring in Waterloo
Bibliographic record
Abstract
In recent decades, the small states of northwestern Europe have been hailed as models of good governance.These societies have used encompassing, cohesive social networks, where "everyone knows everyone," to reform public policies and restructure their economies with remarkable speed.At first glance, these pint-sized success stories would appear to hold few lessons for larger, more heterogeneous polities, where diverse, loosely connected sectors and regions compete to influence national--level outcomes.This paper, however, argues that small cities may resemble small states in their capacity to construct cohesive, cross-sectoral networks.While lacking the fiscal and regulatory tools of a nation--state, reform--oriented, municipal actors can use the "politics of interconnectedness" to accelerate restructuring by constructing collective myths.Focusing on Waterloo, Canada, a poorly resourced, thinly institutionalized environment where collective action should be least likely, the paper demonstrates how policymakers and firms could use the image of Waterloo as an IT leader to rapidly transform the region's industrial base.In doing so, the paper contributes to separate literatures on both small states and cities.In addition to demonstrating how cities can learn from small states, the paper uses regional--level, empirical material to highlight the importance of interpersonal relationships in small, European states.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".