Socio-economic Characteristics of Dissatisfied Users of Wood-based Houses in the Czech Republic
Bibliographic record
Abstract
Although there has been an increasing interest in wooden construction in recent years, this type of constructions is still not as common as in the northern European countries (Sweden, Finland), the US, or Canada. The paper analyses users of wood-based houses in the Czech Republic. The paper presents partial results of this extensive marketing research, analyses dissatisfied users of wood-based houses and identifies socio-economic characteristics of the dissatisfied users. The survey was conducted by researchers from Mendel University in Brno in the year 2012 – 2014 and it covered 1,000 Czech households. Individual factors of perceiving wood-based houseś quality and price were processed both for the satisfied users and the dissatisfied users who would not purchase this type of building again. When processing the data obtained by the research the authors employed various analytical procedures. The prevailing data-analysis tools were basic statistical methods. The results for both groups were mutually compared. The paper deals with differences in socio-economic characteristics of satisfied and dissatisfied users of wooden family houses and makes recommendations for elimination of the number of the dissatisfied users.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".