Pole Vaulting to New High
Bibliographic record
Abstract
Hotel prices continued to gain ground during the recent quarter, regaining losses incurred during the pandemic. Prices in all regions are reverting to their long-term average, with hotels in the Pacific and South Atlantic regions rising above their standardized average. Hotels in both gateway and non-gateway cities continue to exhibit positive performance, although hotels in the gateway cities have posted greater gains. For both large and small hotels, transaction volume increased both quarter over quarter and year over year. Our moving average trendlines indicate that large hotels are priced to buy, while small hotels represent an opportunistic buy at best. Large hotels reached a new statistical high based on our standardized unexpected price (SUP) performance metric. Mortgage financing volume rose, given that financing costs were lower this quarter. Among factors that have contributed to this situation are the facts that the relative risk premium remained stationary this quarter and that the hotel delinquency rate has continued to decline. Our economic value added (EVA) and our shareholder value added (SVA) are positive, indicating that hotel investment based on operating performance is currently financially feasible. Looking toward the next quarter, our leading indicators of hotel price performance indicate that positive price momentum should continue to exist for both large and small hotels.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.076 | 0.015 |
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".