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
Notice bibliographique
Résumé
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/
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,004 | 0,005 |
| Méta-épidémiologie (sens large) | 0,004 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,006 | 0,006 |
| Science ouverte | 0,006 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,031 | 0,004 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».