Security Measures as a Factor in the Competitiveness of Accommodation Facilities
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.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".