Bibliographic record
Abstract
INTRODUCTION SINCE LONDON'S GREAT Exhibition in 1851, cities around the world have vied to showcase themselves and the latest wonders of human creativity by hosting World Fairs or, as they have been called since Montreal in 1967, ‘Expos’. The decision to host an Expo has often reflected a wish to show the growing importance of the country and city hosting it. Expos have also been used to boost local industry and culture and attract tourists. Japan's first Expo, in 1970 in Osaka, was one of the most successful Expos of all time, with 64 million visitors, a number only surpassed by Shanghai, fortyyears later. A ‘portrait’ of that extravaganza by Sir John Pilcher, British ambassador to Japan at that time, was contained in Britain and Japan: Biographical Portraits , volume IX. Japan also hosted smaller, specialised Expos in Okinawa in 1975, Tsukuba in 1985 and Osaka in 1990. The British pavilion at Expo 70 was designed by the Central Office of Information and the UK took part in a small way in the other specialised expos. AICHI 2005 Japan's decision to host a major Expo in 2005 in Nagakute, Aichi prefecture, about 13 km from Nagoya, Japan's fourth city, was the brainchild of Dr Toyoda Eiji, President and later Chairman of the Toyota Motor Corporation. The company's Toyota City car plant lies 20 km from the Expo site. Dr Toyoda had a vision of bringing the world to his hometown. Unlike many Expos, Aichi was located in a relatively rural area, some way out of Nagoya. It was not intended to support urban regeneration. In fact, the theme chosen was ‘Nature's Wisdom’ and, with a strong emphasis on sustainability, pavilions were designed with the aim of minimum temporary or permanent environmental impact. By the end of the twentieth century in the UK and some other countries, there was a growing sense that Expos were an outmoded means of promoting the national brand. In a world of mass global travel, and the rapid dissemination of information via the internet, many – particularly in finance ministries – questioned the benefits of spending money on funding national pavilions at these periodic world fairs. Moreover, Aichi's location, not in one of Japan's mega cities, and not in a hub on the world travel circuit, suggested that the Expo might fail to attract significant numbers of visitors – Japanese or international.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.178 | 0.042 |
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".