Spatiality, Built Form, and Creative Industry Development in the Inner City
Bibliographic record
Abstract
Theoretical treatments of new industry formation within the inner city emphasise the significance of agglomeration economies, social relations, institutional factors, and representational (semiotic) features of the landscape. The author contributes to a larger understanding of generative processes of industrial innovation in the urban core by demonstrating through theoretical synthesis and case studies, the critical roles played by ‘material’ (or physical) space and form. First, Soja's idea of the ‘industry-shaping power of spatiality’ in the new economic spaces of the post-Fordist metropolis is interpreted as the boundedness of inner-city space, the intimacy of urban landscapes, and the integrity of urban form. Second, the cogency of Helbrecht's injunction regarding the complementarity of abstractive and ‘concrete’ dimensions of urban space in knowledge-based industry clusters is demonstrated by case-study references situated in London, Vancouver, and Singapore. Third, the author draws on Markus's incisive analysis of principal building types (and ‘durable taxonomies' of building function and social meaning) to link historic and contemporary industrialisation processes, including the comingling of production, housing, and consumption. The nexus of theoretical integration lies in the articulation of three-dimensional industrial landscapes comprised of physical space and form as well as symbolic constructs.
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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 | 0.002 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".