Bibliographic record
Abstract
What are our needs for happiness? We need to walk, just as birds need to fly. We need to be around other people. We need beauty. We need contact with nature. And most of all, we need not to be excluded. We need to feel some sort of equality. Enrique Penalosa, former mayor of Bogota, UN World Urban Forum, Vancouver, 22 June 2006 Introduction Not many urban leaders rush to proclaim that their city is unfriendly to people. A potential problem with the phrase ‘people-friendly city’ is, then, that, in and of itself, it signifies relatively little. Everyone can sign up to it. The discussion that follows attempts to move beyond bland claims, and identify the main building blocks that might be expected to feature in practical strategies designed to create people-friendly cities. A quotation from William Shakespeare's Coriolanus was used at the beginning of Chapter 7 to highlight the importance of people in any sensible discussion of the city. Sicinius Velutus asks: ‘What is the city but the people?’ This famous dictum provided the prelude to a chapter focussed on the nature of democratic urban governance, and alternative ways of strengthening citizen power in the city. In this chapter we again focus on people and we will, again, refer to the vital importance of citizens having a central role in the decision-making processes that shape the urban environment and local life chances. However, our focus this time will be less on the process of democratic decision-making and more on the substantive outcomes effective place-based leadership aims to deliver. A central theme that will emerge is that, to deserve the name, people-friendly cities have to be inclusive cities. Our starting point is human happiness. The literature on happiness, and the idea of developing public policies that promote happiness, has expanded in recent years (Ben-Shahar 2008; Layard 2011; Montgomery 2013). There are several strands here – some relating to personal growth and development, some relating to public policy and some relating to both. In relation to public policy we can note that there is growing pressure to reconsider what we actually mean by improvements in ‘living standards’ (Sen 1984).
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 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".