Studying Gardens of the World with Students of Higher Education Establishments
Bibliographic record
Abstract
The image of one of the greatest actresses, Audrey Hepburn, is presented in different ways: actress, model, dancer, the Goodwill Ambassador for UNICEF. Audrey Hepburn, who loved nature and gardens, saw a rare opportunity to bring forth their beauty in poetic and meaningful ways in Gardens of the World. Her unique vision of the series included fusing the historical and aesthetic aspects with the arts of literature, music and painting. Gardens of the World was filmed on location around the world, including:- Claude Monet s garden at Giverny; George Washington s Estate at Mount Vernon; the ancient moss temple garden Saiho-ji in Kyoto Japan; gardens at Mottisfont Abbey, Tintinhull House, Chilcombe Garden, Hidcote Bartram Village and Hidcote Manor in England; the Keukenhof Garden and the Tulip Fields of Lisse in the Netherlands, Villa Pancha in the Dominican Republic; Giardini di Ninfa and Villa Gamberaia in Italy; La-Roseraie de L Haÿ-les-Roses, Chateau de Courances, Jardin du Luxembourg, and Jardin du Luxembourg in France. The 8 episodes explore: Roses & Rose Gardens, Formal Gardens, Tulips and Spring Bulbs, Country Gardens Japanese Gardens Flower Gardens, Tropical Gardens, Public Gardens and Trees. Each episode presents a different garden theme as well as broader concepts of aesthetic, botanical, cultural or environmental significance.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.005 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".