Urban filaments: from passageways of leisure-oriented space to emergent urban form
Bibliographic record
Abstract
In the contemporary urban condition there exist pedestrian systems that are not defined as sidewalks or streets -these are the urban filaments that weave themselves into the existing flow structures in historic and more recent urban forms.This paper will trace the challenges facing pedestrian spaces/urban filaments in the modernist experiment with urban form from a European and North American urban context -particularly in the urban cores of the city.Historically urban filaments emerged alongside historical and social changes, spurred on by the growing congestion of major traffic corridors, and responding to the opportunity to provide ameliorative social/cultural space between the existing patterning of public/private space.Innovation in technology and science propelled the form and helped to develop the language of urban filaments along until the automobile challenged the logic of the historic fabric; a largely pedestrian outlook of travel within the built fabric was replaced with a competing vision of the street.This is a tactical position for the city, it needs to continuously absorb and even rediscover various coping mechanisms to counteract the squeeze on pedestrian streetscapes.As a result, there are various qualities and kinds of public/private spaces that are arguably urban filaments, some emerging with other types enduring since before the Renaissance.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".