Bibliographic record
Abstract
From the outset, Vancouver’s successful bid to host the 2010 Winter Olympics was a ‘corporate-civic project’. The agendas of the local growth coalition — city and provincial politicians, as well as major players in the local business community — involved showcasing Vancouver as a destination for global investment, and revitalizing the position of Whistler in the intensely competitive global tourism market. In this, of course, British Columbia (BC) political and business leaders were following a now familiar script in which Olympic Games and other mega-events are understood as opportunities to demonstrate the attractions of a city/region to global visitors and investors. Indeed, pursuing mega-events and promoting them as catalysts for the competitive repositioning of a city is a strategy that has been tried before in Canada, in Montreal and Calgary (Whitson 2004) and in other countries, too (see, for example, Bennett 1991, Whitelegg 2000, Hall 2006, Horne & Manzenreiter 2006). In BC, the provincial government has sought to capitalize on Vancouver 2010 by improving the transportation infrastructure serving Whistler (and other ski resorts, too), and it has viewed this as an investment in the growth of the BC tourism industry (British Columbia 2004).
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 0.007 |
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".