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.
Bibliographic record
Abstract
Purpose The purpose of this paper is to review the extant literature on Airbnb – one of the most significant recent innovations in the tourism sector – to assess the research progress that has been accomplished to date. Design/methodology/approach Numerous journal databases were searched, and 132 peer-reviewed journal articles from various disciplines were reviewed. Key attributes of each paper were recorded, and a content analysis was undertaken. Findings A survey of the literature found that the majority of Airbnb research has been published quite recently, often in hospitality/tourism journals, and the research has been conducted primarily by researchers in the USA/Canada and Europe. Based on the content analysis, the papers were divided into six thematic categories – Airbnb guests, Airbnb hosts, Airbnb supply and its impacts on destinations, Airbnb regulation, Airbnb’s impacts on the tourism sector and the Airbnb company. Consistent findings have begun to emerge on several important topics, including guests’ motivations and the geographical dispersion of listings. However, many research gaps remain, so numerous suggestions for future research are provided. Practical implications By reviewing a large body of literature on a fairly novel and timely topic, this research provides a concise summary of Airbnb knowledge that will assist industry practitioners as they adapt to the recent rapid emergence of Airbnb. Originality/value This is the first paper to review the extant literature specifically about Airbnb.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it