Quantitative analysis of data relating to ski tourism according to Scorpus database
Bibliographic record
Abstract
Background: The aim of the research was to acquire knowledge as to how the development of scientific publications looks, while also to specify the scope of research on the area of ski tourism. The research process is concentrated on the following research questions: In what way has the scientific output developed in the field of research on ski tourism? Who is the principal participant (countries, universities, authors, titles of sources) in the accumulation of research in a particular field? Method: In the process of selecting the test sample as a source of bibliometric data the Scopus database was applied. The test sample (N=1500) consisted of publications that contained such phrases in their works as “ski tourism”, “ski hotels”, “ski resorts” in their titles or key words. A general profiling of publications was conducted in order to assess the trends in scientific output and the acknowledgement of the leading co-workers in the field of research. MS Excel was applied for the purpose of supporting the process of analysis and the visualization of results. Results: The analysis indicates that research on ski tourism is one of the aspects of great tradition. This has gained increasingly great interest among academic environments, which led to the breakthrough growth in the number of publications in 2006 indexed in the Scopus database. This publication output encompasses 26 research areas. Conclusions: The principal areas yielding the largest number of publications with regard to ski tourism are to be found in social sciences and science on the environment. The main authors and co-authors in this field are as follows: representatives from the EU and the USA, while the most productive research institution is Universität Innsbruck. The author of the greatest number of publications is Prof. Daniel Scott (University of Waterloo, Canada). The EU is one of the main sponsors.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".