Sharing Economy in the Accommodation Services – Example of Prague During the Years 2017–2022
Bibliographic record
Abstract
The submitted contribution focuses on the issue of the shared economy in Prague in the period from 2017 to 2022. The main goal is to estimate the daily values of realized offers within the framework of the shared economy and to make a comparison with the traditional accommodation market. The presented analysis of the sharing economy market works with data that are modified by the authors of the contribution so that the desired comparison can be made. These data are based on the number of written user reviews (using Airdna, Airbnb, Insiderairbnb and other sources). Output from this analysis is then converted using an algorithm into the number of realized offers on a daily basis. For the market working with traditional accommodation, data from STR Global was used, which represents the market for accommodation services in Prague based on daily data. The work with daily data is one of the key uniqueness of this contribution, as common studies work only with average data, usually over a longer period of time (a month or a quarter). However, such data are inappropriate for the market of accommodation services. Based on the estimates and analyses, it appears that during the period of the covid-19 pandemic, shared accommodation has become more used than traditional accommodation. This conclusion can be attributed to the situation where many tourists preferred private accommodation over regular accommodation facilities.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".