Developing frontier cities : global perspectives-regional contexts
Bibliographic record
Abstract
Acknowledgements. Introduction. Part One: Frontier Urban Development in a Global Context. 1. Moving Frontiers: A Local-Global Perspective P. Nijkamp. 2. The Frontier Metropolis in the Twenty-First Century D.R. Diamond. 3. Institutional Requirements for New Local, National and Multinational Planning Realities in Europe J.M. Mastop. 4. The Future of Frontier Cities J. Kotek. 5. Living on the Edge: Conditions of Marginality in the Canadian Urban System L.S. Bourne. 6. Innovation Networks, Dynamic Externalities and Peripheral Cities in a Global Context N. Hansen. Part Two: Case Studies. 7. Developing Frontier Cities: Lessons from the Cities of the Prairie Program D.J. Elazar. 8. Modernization and the Mobilization of Public Capital: Developing Pueblo, Colorado, 1960-1997 S.L. Schechter. 9. Globalization and Cities in Frontier Regions: A Case Study of Northern Australia K. O'Connor. 10. Competitive Advantage in Frontier Regions of Europe: Redefining the Global-Local Nexus S.P. Dawe. 11. Alternative Models of Urban Development in Frontier Regions: The Case of Friuli, Italy E. Saraceno. 12. Local Initiatives in Peripheral Areas: An Intercultural Comparison Between Two Case Studies in Brazil and Austria W. Stohr. 13. The Polyurban Frontier in Post-Industrial Israel S.B. Cohen. 14. The Challenge of Industrial Development for Israel's Frontier Cities H. Lithwick, Y. Gradus. 15. New Egyptian Desert Cities D.J. Stewart. 16. Urban Development at theEcuador Amazon Frontier: Boom Towns or Gloom Towns R. Ryder.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".