Modelling the Employment in Tourism – Case Study of Croatia
Bibliographic record
Abstract
According to the data on the share of employees in the category Hotels and similar accommodation in the total employees (16.6% in 2015), it can be concluded that this percentage share is quite significant. In this paper the number of employees in tourism (in the category Hotels and similar accommodation) is modelled and predicted on the basis of monthly data from the period 2005 to 2015, collected from the First Release of the Croatian Bureau of Statistics. Taking into consideration the seasonal character of the phenomenon being analysed, taking into account the criteria of reliability of demonstrated forecasts, in this study following methods were used: the seasonal naive models, Holt - Winters Model trend seasonality exponential smoothing and Holt- Winters no seasonal exponential smoothing model. All obtained results were compared by forecasting error Mean Absolute Percentage error (MAPE). The obtained results indicate that forecasting methods which take into account the seasonal character of the phenomenon result in smaller forecasting error, and more reliable estimate, compared to models which don´t take into account the character of the phenomenon being analysed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".