Bringing the Mountains into the City: Legacy of the Winter Olympics, Turin 2006
Bibliographic record
Abstract
Exactly 50 years after Cortina d’Ampezzo (1956), the Winter Olympic Games returned to Italy in 2006, in Turin, and for the tenth time to the Alpine environment, where they were met with a totally changed context. While in the first, Italy had been a country heading towards Fordist industrialisation and Cortina an élite tourist resort; 50 years later the Olympics came to a city that had been the symbol of Fordist industrialisation in Italy, a one-company town on a par with Detroit, which used the great Olympic event to give a further and decisive thrust to its post-Fordist transition. Turin was a city of around 900,000 inhabitants, closer to 1,700,000 when considering the metropolitan conglomeration. It was near the Alps, with close cultural ties with the Alpine environment, although it was not definable as an Alpine city, unlike Grenoble or Innsbruck. For the Olympic Movement, the choice to host the 20th Winter Olympic Games in Turin was an affirmation of the urban Winter Olympic model, a departure from the standard choice of Alpine tourist resort. And this, as we shall see, was one of the most important characteristics of the Torino 2006 games, that is, laying emphasis on the urban Winter Olympics proposal, closer to the Summer Games than the conventional Winter Games. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".