Bibliographic record
Abstract
Urban regeneration is often cited as one of main justifications for staging the Olympic Games. Regeneration ‘legacies’ have been principally pursued by the hosts of Summer Games, but recent cases suggest they are increasingly relevant to the Winter Games too. There are even examples where urban regeneration has been influenced by losing Olympic bids (Smith, 2012). The aim of this chapter is to explore how and why the Olympic Games are used as a vehicle for regeneration. Through this analysis, the chapter is able to draw important conclusions not only about the contemporary Games, but also about urban regeneration processes in general. Regeneration can be understood as a discourse as well as a practice and an outcome and, as such, this chapter analyses commonly expressed rhetoric such as the notion that staging the Olympic Games provides ‘flagship’ urban projects and ‘catalysts’ for regeneration. The discussion focuses particularly on those Games which were staged on post-industrial sites: Barcelona 1992, Sydney 2000, Vancouver 2010 and London 2012. Rather than discussing these cases in turn, the chapter is organized around themes that help clarify the complex relationship between the Olympic Games and urban regeneration.
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 | 0.000 | 0.000 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.052 | 0.013 |
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".