TREND: Organisation for Economic Co-operation and Development (OECD). Main Economic Indicators (MEI): Labor - Labor Market Statistics | Country: Iceland | Indicator: Labour Force Statistics - Quarterly Levels - Labour Force Statistics - Quarterly Levels - Employment - by economic activity - Employment - by economic activity, Manufacturing - Employment - by economic activity, Manufacturing, All persons, 2003 - 2018. Data Planet™ Statistical Datasets: A SAGE Publishing Resource Dataset-ID: 062-003-008
Bibliographic record
Abstract
Organisation for Economic Co-operation and Development (OECD). Main Economic Indicators (MEI): Labor - Labor Market Statistics | Country: Iceland | Indicator: Labour Force Statistics - Quarterly Levels - Labour Force Statistics - Quarterly Levels - Employment - by economic activity - Employment - by economic activity, Manufacturing - Employment - by economic activity, Manufacturing, All persons, 2003 - 2018. Data Planet™ Statistical Datasets: A SAGE Publishing Resource Dataset-ID: 062-003-008 Dataset: This subset of the Main Economic Indicators (MEI) database includes data on employment rates. Values are expressed as a percentage. The Organisation for Economic Co-operation and Development (OECD) Main Economic Indicators database provides comparative statistics on OECD member countries and other non-member countries. Data availability varies by nation and by time period. See the technical documentation for information on national practices related to the compilation of data. http://www.oecd-ilibrary.org/economics/data/main-economic-indicators_mei-data-en Category: Labor and Employment, International Relations and Trade Subject: Private Sector, Employment, Economic Development, Economic Conditions, Manufacturing Industry Source: Organisation for Economic Co-operation and Development (OECD) Established in 1961, when 18 European countries plus the United States and Canada joined together to create an organization dedicated to global development, the Organisation for Economic Co-operation and Development (OECD) today includes 34 member countries from around the globe, ranging from North and South America to Europe and the Asia-Pacific region. Member countries include many of the world’s advanced countries as well as emerging nations. The OECD mission remains the promotion of policies that will improve the economic and social well-being of people around the world. The OECD collects and analyzes data on a broad range of topics to help governments foster prosperity and fight poverty through economic growth and financial stability, at the same time taking the environmental implications of economic and social development into account. The OECD Secretariat collects and analyzes data, after which committees discuss policy regarding this information, the Council makes decisions, and then governments implement recommendations. The performance of individual countries is monitored following implementation via a system of multilateral surveillance and a peer review process. The OECD is headquartered in Paris, France, and it is funded by its member countries. National contributions are based on a formula that takes account of the size of each member's economy. The largest contributor is the United States, which provides nearly 24% of the budget, followed by Japan. http://www.oecd.org.ezproxy.liberty.edu/
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.
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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.008 | 0.028 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.152 | 0.214 |
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".