Bibliographic record
Abstract
The World Furniture Outlook 2016-2017 by CSIL provides an overview of the world furniture industry with historical statistical data (production, consumption, imports, exports) and 2017 furniture markets scenario for 70 countries. This market research report also includes: Growth of furniture imports worldwide and the role of furniture exporting countries in the marketplace Market shares of the major furniture exporters are provided by geographical region Analysis of the opening of furniture markets that covers the past nine years, with trade balance, imports/consumption and exports/production ratio data. Statistics and outlook data are also available in a country format: origin of furniture imports destination of furniture exports historical series on furniture production historical series on furniture market size historical series on furniture trade country rankings to place all statistics in a broad worldwide context. The seventy country tables have been expanded to include three additional items: Total household consumption expenditure (in billions of US$) Total GNP at purchasing power parity (in billions of US$) Per capita GNP at purchasing power parity (in US$) Key issues of the World Furniture Outlook 2016-2017 market research report: a picture of opportunities for furniture exporters arising from the increasing openness of markets a rich collection of key country-data, allowing comparisons among specific interest areas. prospects of world furniture trade in 2016-2017, 2016 and 2017 forecasts on the evolution of furniture markets in the considered countries, based on the analysis of furniture industry dynamics and of macro-economic indicators Countries covered (selected according to their contribution to production and international trade of furniture): Algeria, Argentina, Australia, Austria, Bahrain, Belgium, Bosnia-Herzegovina, Brazil, Bulgaria, Canada, Chile, China, Colombia, Croatia, Cyprus, Czech Republic, Denmark, Egypt, Estonia, Finland, France, Germany, Greece, Hong Kong, Hungary, Iceland, India, Indonesia, Ireland, Israel, Italy, Japan, Kazakhstan, Kuwait, Latvia, Lebanon, Lithuania, Malaysia, Malta, Mexico, Morocco, Netherlands, New Zealand, Norway, Oman, Philippines, Poland, Portugal, Qatar, Romania, Russia, Saudi Arabia, Serbia, Singapore, Slovakia, Slovenia, South Africa, South Korea, Spain, Sweden, Switzerland, Taiwan, Thailand, Turkey, Ukraine, United Arab Emirates, United Kingdom, United States, Venezuela, Vietnam.
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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 | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.057 | 0.089 |
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".