Podaż najmu krótkoterminowego w województwie wielkopolskim na przykładzie użytkowników serwisu Airbnb
Bibliographic record
Abstract
Short-term rental has become an alternative to the traditional hotel offer and is, to an increasing extent, becoming the first choice for travelers around the world. The main objective of this paper is to present the supply aspect of the short-term rental market in the Greater Poland Voivodeship. The analysis used data from the Airbnb website, which enables the rental of premises for days from private persons. The diversification of short-term rental supply in the voivodship was presented in the poviat system and covered the period from September 2021 to August 2022. The facilities offered for short-term rental in the region were usually larger in area than typical forms of accommodation in registered accommodation establishments. In line with the nationwide and global trend, most of the facilities were concentrated either in large cities and their immediate location, or in areas attractive to tourists. The location of the properties offered on the Airbnb platform was consistent with the location of registered accommodation facilities. The supply of short-term rental in the voivodship was most often one or two-room apartments or houses, less often studio properties or large facilities with at least five rooms. The properties were fairly well equipped with access to a kitchen. Most of the offers guaranteed a parking space and Internet access. In most counties, Airbnb properties were usually only rented for a maximum of a quarter of the year, and the hosts did not set a minimum rental period. The average fee per night in a rented property varied, but it did not exceed PLN 500. Despite the voices that criticize the development of short-term rental in the tourism industry due to the creation of dangerous socio-economic processes on the occasion of changes in the urban fabric of cities or unfair competition for hotel operators, the supply of short-term rental in the Greater Poland Voivodeship seems to be an interesting alternative to the offer of the hotel industry.
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How this classification was reachedexpand
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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.010 | 0.012 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.015 | 0.011 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.367 | 0.096 |
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; both teacher heads agree on what is shown here.
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".