Bibliographic record
Abstract
Preface U.S. Travel & Industry Travel & Spending Grows in Second Quarter 2010 International Visitation to the United States: A Statistical Summary of U.S. Arrivals (2009) Top 10 International Markets: 2009 Visitation & Spending Testimony of Mary Saunders, Acting Assistant Secretary for Manufacturing & Services, International Trade Administration, Dept. of Commerce, before the Senate Commerce, Science & Transportation Subcommittee, Hearing on in Troubled Times Testimony of Jay S Witzel, President & CEO, Carlson Hotels Worldwide, before the Senate Commerce, Science & Transportation Subcommittee, Hearing on The National Economic Impact of Travel & Tourism Testimony of Sam Gilliland, Chairman & CEO, Sabre Holdings Corporation, before the Senate Commerce, Science & Transportation Subcommittee, Hearing on in Troubled Times Testimony of Jay Rasulo, Chairman, Walt Disney Parks & Resorts, before the Senate Commerce, Science & Transportation Subcommittee, Hearing on in Troubled Times Testimony of Rossi Ralenkotter, President & CEO, Las Vegas Convention & Visitors Authority, Las Vegas, Nevada, before the Senate Commerce, Science & Transportation Subcommittee, Hearing on in Troubled Times Restoring America's Travel Brand: National Strategy to Compete for International Visitors Index.
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 | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.258 | 0.129 |
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".