Bibliographic record
Abstract
The Ministry of Tourism of the province of Quebec benefits from working hand-in-hand with a Montreal-based not-for-profit organization whose mandate is to make tourism and culture accessible to persons with limited physical abilities. Created in 1979, Keroul is funded by its membership, donations and various government ministries and agencies. In 2009 the Quebec Ministry of Tourism renewed a three years commitment to Keroul, allocating it $100,000 of its annual budget. The key goal of Keroul is to standardize development, implement and promote accessibility for persons with physical disabilities. Over the course of this presentation, the author will introduce the major steps taken by the Quebec Ministry of Tourism, working hand-in-hand with Keroul, to improve and promote accessibility of the tourism destinations located in the province of Quebec. Data on the importance of this market segment is slowly becoming available. This presentation will include as much data as possible in hopes that it will be useful in drawing the reader's own conclusions as the importance of addressing this growing tourist segment of persons with physical disabilities. Having been, for the last 20 years, a member of the core team that oversees the roadway signage program of the Quebec tourism industry, the author has witnessed, again and again, the benefits gained from seemingly insignificant improvements to facilities, such as proper signage, designated areas, ramps and the like, and can only encourage you to do the same. Money well spent!
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.132 | 0.012 |
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".