Bibliographic record
Abstract
Since the 1990s, interest in place (as opposed to space) has surged across a spectrum of social science disciplines including planning. But the empirical focus has been chiefly on cities along the Atlantic Rim even as vast new areas in Asia, Africa, and Latin America were undergoing accelerated urbanization. This essay outlines a planning perspective to global place-making in the face of fierce inter-city competition for footloose capital. The question of how a place can be defined, and what criteria might serve to delineate a place occupies the first part of this essay. The definition proposed encompasses both a physical/built environment at the neighborhood scale and the subjective feelings its inhabitants harbor towards each other as an emplaced community. Specific criteria are discussed, with brief illustrations from Taiwan and China. But the art of place-making has not informed planners of the swaths of the urban in the newly industrializing global regions of Asia and elsewhere. Their principal preoccupation has been with the branding of cities and the advanced infrastructure required by global capital. In the process, millions of ordinary folks have been displaced and their neighborhoods erased, as speed, movement, and power have been valued more than the fragile social infrastructure of place-based communities. The essay concludes with an argument that place-making is everyone's job, local residents as well as official planners, and that old places can be “taken back” neighborhood by neighborhood, through collaborative people-centered planning. Examples from Japan, China, and Canada are used to illustrate these propositions.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".