Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The twenty-fifth edition of CSIL research 'World trade of lighting' is the result of: official trade data; analysis of CSIL databases for lighting fixtures in Europe and worldwide; official documents concerning macroeconomic trends and sector performances for the last two years. The Report provides an overview of the world trade of lighting fixtures and lamps, with statistical data (production, consumption, imports, exports) at worldwide level and data on the lighting fixtures markets of 70 countries selected according to their contribution to the international trade of lighting fixtures. The report identifies the opportunities on the global lighting fixtures market and is a helpful tool for lighting fixtures exporters as it contains a rich collection of key country data, allowing comparisons among specific interest areas. The world production, consumption, imports and exports of lighting fixtures are broken down by geographical area (European Union 25 + Norway and Switzerland, Central-East Europe outside the EU & Russia, Asia and Pacific, Middle East and Africa, North and South America). International trade statistics (imports and exports) 2014-2019 are given at country-level both for lighting fixtures and lamps, highlighting the major exporting and importing countries and providing main destination countries of exports and main origin countries of imports. The report also analyses trade balance data, covering the years 2014-2019. Data are provided both in US$ and EUR. Data are also available in a country format: production, consumption, trade figures, and per capita consumption of lighting fixtures. Historical series on lighting fixtures exports and imports, origin of imports and destination of exports, by country and by geographical area. International trade data of lamps. Economic indicators (population, area, GNP, per capita GNP, household consumption expenditure) and forecasts 2020 and 2023 (population, GNP, private consumption, and lighting market). Country rankings to place all statistics in a broad worldwide context and allowing comparisons among specific interest areas. Countries considered: Algeria, Argentina, Australia, Austria, Bahrain, Belarus, Belgium, Brazil, Bulgaria, Canada, Chile, China, Colombia, Croatia, Cyprus, Czech Republic, Denmark, Egypt, Estonia, Finland, France, Germany, Greece, Hungary, India, Indonesia, Ireland, Israel, Italy, Japan, Jordan, 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, Tunisia, Turkey, Ukraine, United Arab Emirates, United Kingdom, United States, Venezuela, Vietnam.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it