Bibliographic record
Abstract
Since London's Great Exhibition of 1851, world's fairs and international expositions have been an important global cultural phenomenon that has defined progress and modernity for hundreds of millions of visitors.They have displayed the achievements of industrial civilization and the latest technologies, while at the same time reinforcing national and ethno-cultural hierarchies, with exhibits ranging from scientific discoveries to human zoos.The world's fairs have also generated an impressive amount of documentation and material artifacts.The sheer variety of materials produced in tandem with the fairs is well represented in this online resource, which makes accessible an abundance of digitized materials relating to the world's fairs from archives and libraries in Canada, France, the United Kingdom, and the United States, including the vast Donald G. Larson world's fair collection at California State University, Fresno.World's Fairs: A Global History of Expositions comprises not only official documents, correspondence, catalogues, and other publications from the fairs, but also diaries, souvenirs, posters, postcards, songs, music scores, and sound recordings of interviews and music.The interviews are fascinating and offer insights into how visitors experienced the world's fairs, or at least how they remembered their experiences.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".