Bibliographic record
Abstract
Everyday streets are both the most used and most undervalued of cities’ public spaces. They are places of social aggregation, bringing together those belonging to different classes, genders, ages, ethnicities and nationalities. They comprise not just the familiar outdoor spaces that we use to move and interact but also urban blocks, interiors, depths and hinterlands, which are integral to their nature and contribute to their vitality. Everyday streets are physically and socially shaped by the lives of the people and things that inhabit them through a reciprocal dance with multiple overlapping temporalities. The primary focus of this book is an inclusive approach to understanding and designing everyday streets. It offers an analysis of many aspects of everyday streets from cities around the globe. From the regular rectilinear urban blocks of Montreal to the military-regulated narrow alleyways of Naples, and from the resilient market streets of London to the crammed commercial streets of Chennai, the streets in this book were all conceived with a certain level of control. Everyday Streets is a palimpsest of methods, perspectives and recommendations that together provide a solid understanding of everyday streets, their degree of inclusiveness, and to what extent they could be more inclusive.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.059 | 0.021 |
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".