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Enregistrement W1837712515

Youth and the Millennium Development Goals: Challenges and Opportunities for Implementation

2005· article· en· W1837712515 sur OpenAlexaff
Amir Farmanesh, Melanie Ashton, Luis Davila Ortega, Emily Freeburg, Catherine Kamping, Cameron Neil, Solange Marquez, Richard Bartlett, Nick Moraitis

Notice bibliographique

Revuenon disponible
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueHuman Rights and Development
Établissements canadiensUniversity of Toronto
Organismes subventionnairesnon disponible
Mots-clésMillennium Development GoalsPolitical scienceEconomic growthPoliticsWork (physics)Public relationsScale (ratio)Positive Youth DevelopmentDeveloping countryGeographyEngineeringEconomics
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Young people ages 15 to 24 are 1.2 billion of the world's human capital. Around the world, many of them are already making contributions to the Millennium Development Goals (MDGs), and their work should be further acknowledged and strengthened. Increasingly, youth are recognized as key participants in decision-making and development, as reflected in the growing presence of non-governmental youth organizations and the upsurge of youth advisory boards and committees to international institutions and programmes. Yet building the capacity of and creating sustained partnerships with young people are crucial strategies to achieving the MDGs that have not been fully realized by the international community.This paper aims to provide an overview of youth participation as it currently exists, to outline the ways in which youth are directly involved and affected by each Goal, to demonstrate the ways in which young people are contributing to the MDGs, and to provide 'Options for Action' that governments, the United Nations system, donors and other actors can harness, support, and scale-up in order to support young people in making significant contributions to achieving the MDGs.Part I outlines the existing mechanisms for youth participation in development policy. These channels can be used by governments and institutions to strengthen and mobilize young people as partners in policy formulation. Successful modes of participation should be recognized and replicated, and also adapted to the challenging political and socio-economic realities facing many youth-led and youth-serving organizations.Part II presents youth participation as it relates directly to the MDGs. Each goal is analyzed with respect to its effect on young peoples lives as well as how young people can play - and indeed are playing - a role in its implementation. Under each goal are a number of for that governments, the UN and multilateral organizations can use to fully harness the contributions that youth can make to achieving the MDGs.Part III outlines the synergies between the Options for Action presented in this report and the Quick Wins proposed by the Millennium Project. The Options for Action are complimentary and provide a process to implement the Quick Win actions, using young people as key implementing agents and service providers. Part III also outlines a number of youth-focused Quick Wins that can make a significant and measurable difference to the state of young people in target countries.Part IV elaborates on how youth can participate in achieving the MDGs and contains cross-cutting recommendations on youth engagement in all 8 Goals.Overall, the report demonstrates that investing in youth will provide the longest and most effective dividend towards meeting the Millennium Development Goals (MDGs) by building the social capital needed to foster pragmatic development. Indeed, without the involvement of young people, a demographic that comprises one fifth of the world's total population, the full achievement of the MDGs will remain elusive and their long-term sustainability will be compromised. Youth participation is currently quite varied, ranging from effective, to sometimes tokenistic, to often non-existent. There are specific ways in which youth and youth organizations can contribute to the design and implementation of MDG-based strategies, some of which are outlined in this document. Many projects are already happening, but there is much work left still to be done.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,745
Score d'incertitude au seuil0,475

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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 Opus0,199
Tête enseignante GPT0,351
Écart entre enseignants0,152 · 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 tête enseignante, 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
GenreEmpirique

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

En bref

Citations5
Publié2005
Routes d'admission1
Résumé présentoui

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