Catching up: understanding the pursuit of major games by rising developmental states
Bibliographic record
Abstract
As a category, Developmental States (DS) in the world political economy express a distinctive set of ideological practices that give them specific incentives to host sporting mega-events. Asian DSs, or ‘late(r) developers’, in particular offer interesting case studies by which to explore the symbolic value such states attach to hosting Olympic Games, Commonwealth Games, and other major sporting events. This paper examines five Asian examples (Tokyo Olympic Games 1964, Seoul Olympic Games 1988, Beijing Olympic Games 2008, Kuala Lumpur Commonwealth Games 1998, and Delhi Commonwealth Games 2010) from the past half century which, despite their variations, illustrate the role of mega-event hosting among DSs and, specifically, the effect these events are intended to have in signalling conceptions of modernity and legitimacy to the international community and world society on behalf of their host cities/countries. Despite thenearly ubiquitous claims to economic growth and development, which accompany the bidding and preparation for major games, it is the symbolic representations of modernity and national ‘success’ that are most commonly pursued among this class of hosts.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".