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Record W7015680823

US Hotel Industry Performance Outpacing Expectations

2021· article· en· W7015680823 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueVTechWorks (Virginia Tech) · 2021
Typearticle
Languageen
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)Economic recoveryHotel industryOvertimeRestructuringStimulus (psychology)Hospitality industryCoronavirus disease 2019 (COVID-19)
DOInot available

Abstract

fetched live from OpenAlex

Amanda Hite, President of STR, noted that her team is pleasantly surprised they were wrong with their overall pandemic recovery forecast for quarter one of 2021. The rollout of vaccinations and increased stimulus spending has been extremely helping for the hotel industry. However, experts remain concerned regarding the ADR recovery and where that will lead the industry rates. If the ADR does not drop, recovery for the industry will move faster. Another key factor will be the comeback of transient demand, which Hite explained is predicted to be down 40-50% of 2019 levels in the fourth quarter of 2021. The industry must continue to see an uptick in transient demand to survive this challenging period.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.020
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesBibliometrics, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.453
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0240.174
Science and technology studies0.0010.000
Scholarly communication0.0030.001
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.002

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.

Opus teacher head0.408
GPT teacher head0.527
Teacher spread0.119 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it