The Analysis of Association between the Variables in Croatian Business Survey for Services Sector
Bibliographic record
Abstract
The aim of this paper was to analyse the association between the variables in Croatia's Business Survey for the Services Sector. The survey was conducted for the 3rd quarter of 2014. Since the Business Survey is a qualitative survey, nonparametric measures of association were used. The empirical analysis was divided into two parts. The first part of the research includes the analysis of association between the variable liquidity as an important variable in the Croatian economy in recession conditions and the selected variables such as business position, demand and firm’s total employment over the past 3 months, and expected business position, demand and firm’s total employment in the next 3 or 6 months. In the second part, the relationships between past and expected business position, demand and firm’s total employment were explored. The results show that there are no statistically significant associations between variable liquidity and variables current and expected demand, firm’s total employment and business position. On the other hand there are statistically significant relationships between current and expected business position, as well as between demand and total employment, past and expected. Association between managers’ estimates and expectations suggests that the same dynamic of variables is expected in the next 3 to 6 months. Since their assessment of current business position, employment and turnover are unfavorable, it can be concluded that Croatian economy is going to retain in recession in the next several months.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".