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Record W2909816726 · doi:10.1080/10941665.2018.1564342

Do Airbnb’s “Superhosts” deserve the badge? An empirical study from China

2019· article· en· W2909816726 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAsia Pacific Journal of Tourism Research · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSharing Economy and Platforms
Canadian institutionsMcMaster University
FundersNational Natural Science Foundation of China
KeywordsChinaAdvertisingBusinessEmpirical researchTourismMarketingGeographyMathematics

Abstract

fetched live from OpenAlex

The “Superhost” badge is that Airbnb entitles the host provides good services. This study verifies Airbnb’s “Superhost” mechanism by applying text mining technologies, combined with Long Short-Term Memory (LSTM) and K-Means, to the entire dataset of tourists’ online reviews of Hangzhou city, China. Six kinds of hosts’ good services are identified, including “Three Meals or Night Snacks,” “Fruits, Drinks or Snacks,” “Travel Guides,” “Free Shuttle or Helping with Luggage,” “Chats,” and “Replies or Communications.” The study reveals the minority of “Superhosts” are mentioned of providing the majority of six kinds of good services, which means “Superhosts” do deserve the badge.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient 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 score0.053
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.072
GPT teacher head0.345
Teacher spread0.273 · 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