Bibliographic record
Abstract
Airbnb has become a frequent option when people start to plan their trips. The trend of Airbnb usage has shown that people are switching from regular hotel booking platforms like Expedia, Bookings, etc., to Airbnb while traveling to a new place. Airbnb is not only a platform that offers new options for hotel booking but also a lifestyle-sharing platform that allows travelers to share their living experiences from different destinations. What’s more interesting is that even during the Covid-19 pandemic, when people cannot travel or go outside, they can still interact with homeowners (hosts) on their chosen topic via streaming video. Therefore, those contents successfully attract more users who become passionate about the Airbnb experience and are willing to share their comments or reviews under each topic or location. This paper focuses on the renting industry. We select Airbnb and try to find the relationships behind different variables. We want to look at our database and use different languages to understand Airbnb in New York, such as multiple regression analysis. We have a database before the Covid-19 outbreak, which can fairly reflect the situation during the normal time. In order to find the relatively accurate correlation results, we want to confirm the correlations which confidently proclaimed that ‘calculated_host_listings_count’ has positively correlated with ‘Reviews_per_month’. This paper aims to identify the key elements that most impact customers submitting their monthly reviews and how those reviews motivate other customers to book their upcoming trips through Airbnb continuously.
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 | 0.002 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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.
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".