International Perspectives on Age-Friendly Cities
Bibliographic record
Abstract
Foreword: International Perspectives on Age-Friendly Cities John R. Beard and Lisa Warth. Acknowledgments. Introduction: International Perspectives on Age-Friendly Cities Kelly G. Fitzgerald and Francis G. Caro Part I: Case Studies: Cross-Cutting Themes. Europe. 1. Developing Age-Friendly Cities: Case Studies from Brussels and Manchester and Implications for Policy and Practice Tine Buffel, Paul McGarry, Chris Phillipson, Liesbeth De Donder, Sarah Dury, Nico De Witte, An-Sofie Smetcoren and Dominique Verte 2. Ireland's Age Friendly Cities and Counties: The Development of a National Program Sinead Shannon and Hugh O'Connor. Asia. 3. From Age-Friendly Research to Age-Friendly City and Age-Friendly Regional Network: Case of Tuymazy and Republic of Bashkortostan, Russian Federation Gulnara A. Minnigaleeva 4. Successful Aging in High-Density City State: A Review of Singapore's Aging Policies and Urban Initiatives Keng Hua Chong, Zheng Jia, Debbie Loo and Mihye Cho. Canada. 5. Lessons Learned from a Canadian, Province-Wide Age-Friendly Initiative: The Age-Friendly Manitoba Initiative Verena H. Menec, Sheila Novek, Dawn Veselyuk and Jennifer McArthur 6. Age-Friendly City in Quebec (Canada), or Alone It Goes Faster, Together It Goes Further Suzanne Garon, Mario Paris, Andreanne Laliberte, Anne Veil and Marie Beaulieu. United States 7. Portland, Oregon: A Case Study of Efforts to Become More Age Friendly Alan DeLaTorre and Margaret B. Neal 8. The Environment of Environmental Change: The Impact of City Realities on the Success of Age-Friendly Programs Allen Glicksman and Lauren Ring Part II: Special Topics 9. Commentary: Changing Practice and Policy to Move to Scale: A Framework for Age-Friendly Communities across the United States M. Scott Ball and Kathryn Lawler 10. Transforming the Way We Live Together: A Model to Move Communities from Policy to Implementation Laura Keyes, Deborah R. Phillips, Evelina Sterling, Tyrone Manegdeg, Maureen Kelly, Grace Trimble and Cheryl Mayerik 11. Making the Right Moves: Promoting Smart Growth and Active Aging in Communities Kathleen E Sykes and Kristen N. Robinson 12. Does the Village Model Help to Foster Age-Friendly Communities? Andrew E. Scharlach, Joan K. Davitt, Amanda J. Lehning, Emily A. Greenfield and Carrie L. Graham 13. Multigenerational Planning: Integrating the Needs of Elders and Children Mildred E. Warner and George C. Homsy 14. Age-Friendly Community Policy Innovation: Complete Streets Implementation in Louisiana, United States Billy Fields and Jason Tudor 15. Age-Friendly Cities and the WHO Checklist: Lessons from a Portuguese Survey Alexandra Lopes, Teresa Pinto and Rute Lemos. Appendix to Chapter 15: Age-Friendly Cities and the WHO Checklist: Lessons from a Portuguese Survey Alexandra Lopes, Teresa Pinto and Rute Lemos
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.036 | 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".