Security Measures as a Factor in the Competitiveness of Accommodation Facilities
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
The main aim of this article was to assess whether the level of competitiveness of accommodation facilities results from the level of safety and security provided to consumers of these services, measured by the number of security measures applied in them. The authors’ task was to examine the level of concentration of security measures in the accommodation facilities and to assess whether the quality of services measured by the star-rating system provided a higher level of safety and security for customers of the accommodation facilities, measured by the number of security measures applied in them. It was decided to examine whether the level of concentration of security measures at the accommodation facilities was treated by these entities as a factor of their competitiveness. Two locations in Central and Eastern Europe, one in Poland and one in Lithuania, were analyzed. The article calculated the frequency of these measures at the accommodation facilities by type of facility (according to the star-rating system) and type of security measure (as a weighted average) and their concentration using the Herfindahl–Hirscham Index. The results showed that the higher the quality of services provided (more stars), the higher the level of safety and security is ensured. It was also found that a higher level of security was not reflected in the prices of accommodation services.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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