MétaCan
Menu
← Retour à la cohorte
Enregistrement W6901602065 · doi:10.6068/dp1651de1896a0

TREND: Organisation for Economic Co-operation and Development (OECD). OECD Factbook 2014: Economic, Environmental and Social Statistics: Education - International Student Assessment | Country: Belgium | Socioeconomic Indicator: Standard Error on the Mathematics Scale in PISA 2012: Men, 2012. Data Planet™ Statistical Datasets: A SAGE Publishing Resource Dataset-ID: 062-001-007

2018· other· en· W6901602065 sur OpenAlexaboutno aff

Notice bibliographique

RevueData Planet · 2018
Typeother
Langueen
Domaine
Thématique
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésSocioeconomic statusScale (ratio)Reading (process)LiteracyPublishingResource (disambiguation)Variety (cybernetics)International comparisons

Résumé

récupéré en direct d'OpenAlex

Organisation for Economic Co-operation and Development (OECD). OECD Factbook 2014: Economic, Environmental and Social Statistics: Education - International Student Assessment | Country: Belgium | Socioeconomic Indicator: Standard Error on the Mathematics Scale in PISA 2012: Men, 2012. Data Planet™ Statistical Datasets: A SAGE Publishing Resource Dataset-ID: 062-001-007 Dataset: Presents mean scores and standards of error on reading, mathematics, and science scales from the Organisation for Economic Co-operation and Development (OECD) Programme for International Student Assessment (PISA) 2012. The PISA assesses student knowledge and skills at age 15. PISA is a triennial survey of 15-year-old students around the world and covers three main subjects: mathematics, reading and science, with one of these treated as the major domain in each round. In PISA 2012 the major domain was mathematics. For PISA, mathematical literacy means the capacity to formulate, employ and interpret mathematics in a variety of contexts to describe, predict and explain phenomena. It assists individuals in recognizing the role that mathematics plays in the world and to make the well-founded judgments and decisions needed by constructive, engaged and reflective citizens. Reading literacy is the capacity to understand, use and reflect on written texts in order to achieve one's goals, develop one's knowledge and potential, and participate in society. Scientific literacy is the capacity to use scientific knowledge to identify questions, acquire new knowledge, explain scientific phenomena, and draw evidence-based conclusions about science-related issues. Data are reported by gender. This dataset provides indicators included in the OECD Factbook 2014: Economic, Environmental, and Social Statistics, updated annually by the Organisation for Economic Co-operation and Development (OECD). Indicators, reported in 12 broad subject areas, cover a wide range of topics: agriculture, economic production, education, energy, environment, foreign aid, health, industry, information and communications, international trade, labor force, population, taxation, public expenditure, and research and development. Data are provided for all OECD member countries and Brazil, China, India, Indonesia, Russia, and South Africa, where available. NOTE: The data presented here are copyrighted by OECD and reproduction is subject to OECD permissions policies: See http://www.oecd.org/rights for further information. Indicator descriptions are based on the OECD Factbook 2014. http://stats.oecd.org/BrandedView.aspx?oecd_bv_id=factbook-data-en&doi=data-00590-en Standards of error are reported because results are based on probability samples. Category: Education, International Relations and Trade Subject: Reading, Students, Sciences, Mathematics, Educational Achievement, Math, Males, Females 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/

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,035
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Jeu de données · Signal consensuel: Jeu de données
Score de désaccord entre enseignants0,151
Score d'incertitude au seuil0,504

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0040,035
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0040,002
Bibliométrie0,0120,047
Études des sciences et des technologies0,0010,001
Communication savante0,0070,005
Science ouverte0,0040,004
Intégrité de la recherche0,0020,005
Charge utile insuffisante (le modèle a refusé de juger)0,1510,182

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.

Tête enseignante Opus0,021
Tête enseignante GPT0,310
Écart entre enseignants0,289 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreJeu de données

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2018
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueData Planet→Travaux en français237 207→