Disputing the Character of the City: Heritage, Regeneration and the Urban Design Turn
Bibliographic record
Abstract
Like many cities with an abundant legacy of heritage structures and aspirations to expand their economic and population base, Halifax (Nova Scotia) experiences significant tensions between heritage conservation and urban development ambitions. On the one hand, Halifax has a vigorous heritage movement spawned in the wake of slum clearance and urban redevelopment efforts in the 1960s; heritage advocates work consistently to conserve the low-rise character of the historic city. On the other hand, it has an emergent urban design lobby supported by economic development interests and creative class ideas; development advocates call for signature high-rise buildings to attract investment and young people. With each new development proposal, community groups argue about the meaning of past and future, the nature of cultural identity and the image of the city. In this essay we examine the recent emergence of a social network of young urban professionals whose influence is growing rapidly in local debates about urban regeneration. Whereas a decade ago heritage conservation enjoyed high priority in planning debates in many parts of the world, today it competes with arguments for signature architecture and greater urban density. The urban design turn reflects changing cultural priorities but also reveals the operation of new governance mechanisms within local growth machines.
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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 | 0.001 | 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 | 0.015 | 0.064 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".