Notice bibliographique
Résumé
The United Nations Sustainable Development Goals initiative has the potential to set the direction for a future world that works for everyone. Approved by 193 United Nations member countries in September 2016 to help guide global and national development policies in the period to 2030, the 17 goals build on the successes of the Millennium Development Goals, but also include new priority areas, such as climate change, economic inequality, innovation, sustainable consumption, peace and justice. Assessed against common agreed targets and indicators, the goals should facilitate inter-governmental cooperation and the development of regional and even global development strategies. However, each goal presents considerable challenges in terms of collecting and analysing relevant data and producing the statistics needed to measure progress. Most governments in lower resourced countries simply do not yet have the systems and controls in place to produce high quality, reliable data and statistics, and it is questionable whether the quality and integrity of the available information is adequate to support meaningful decisions and set direction for the future. There are substantial implications: where progress cannot be measured accurately because of inadequate or flawed statistics, the result can be misguided decisions, doubts about achievement of the goals and significant wasted resources. Getting statistics ‘right’ depends upon the quality and integrity of the data used to produce them and on the quality of the processes for collecting, manipulating and analysing the data. Without a documentary records as evidence of how the data were gathered and analysed or how statistics were produced and disseminated, it is not possible to confirm that the statistics are complete, accurate and relevant. Various global organisations do recognise the importance of high quality data and statistics for measuring the SDG indicators reliably, but there has been little attention to the role of records in providing the evidence needed to trust the data and statistics. There is, moreover, a lack of awareness that digital information simply will not survive without policies and procedures to manage and preserve it through time. As a result, digital data, statistics and records are being lost regularly on a large scale, particularly in lower resource countries, where the structures needed to protect and preserve them are not yet in place. This book explores, through a series of case studies, the substantial challenges for assembling reliable data and statistics to address pressing development challenges, particularly in Africa. Hopefully, by highlighting the enormous potential value of creating and using high quality data, statistics and records as an interconnected resource and describing how this can be achieved, the book will contribute to defining meaningful and realistic global and national development policies in the critical period to 2030.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,016 | 0,133 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,008 | 0,011 |
| Communication savante | 0,028 | 0,020 |
| Science ouverte | 0,004 | 0,013 |
| Intégrité de la recherche | 0,013 | 0,022 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,175 | 0,164 |
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; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».