Re-constructing inner cityscapes as spaces of consumption
Bibliographic record
Abstract
The Covering Statement reviews the author's publications since 2000, and demonstrates his contribution to urban studies concerning leisure, tourism and regeneration. The work is discussed in three sections that represent the main stages in its development, with the following aims: a) To investigate how place-marketing at the micro-scale can re-present cityscapes in disadvantaged areas as spaces for leisure and tourism consumption to desired target markets, especially higherspending visitors; b) To explain the processes that refashion inner cityscapes as 'ethnic cultural quarters', with critical examination of the effects on social inclusion and exclusion in the public realm, and how public engagement is incorporated into urban design; c) To compare developments in areas fringing city centres in London and other European cities with their counterparts in North America from the mid-1990s. A methodology derived from grounded theory was developed and applied through longitudinal studies that included Brick Lane, London EC 1, and its reimaging as 'Banglatown'. Transcripts of interviews with practitioners responsible for implementation were compared with one another, and with the discourse of public policy. Further comparisons were made through observations and photography of changing urban landscapes, and through analysis of descriptions in guidebooks and promotional material. In the UK and in Canada, urban policy has encouraged leisure and tourism as a catalyst to urban regeneration, and the research confirmed that in both countries collaborations between local authorities and non-state agencies have facilitated rapid growth of urban visitor economies in some inner urban and inner suburban localities. However, it also revealed processes through which, contrary to the intention of public policy, small area-based and short-term structures of urban governance have allowed powerful agencies to influence re-imaging strategies as well as physical reconstruction of the public realm to their commercial advantage. In some cases, such processes have perverse and unintended consequences for less powerful groups. The research demonstrated how Geographic Information Systems for Participation (GIS-P) can be adapted and used to capture insights, views and preferences of people that public agencies consider disadvantaged and 'hard-to-reach' by more established forms of consultation, and who are the intended beneficiaries of regeneration programmes. Thus, it may be incorporated as a technique by urban authorities to accommodate a broader range of interests, and to inform solutions that support their policy aspirations.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.018 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".