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Record W1837712515

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

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

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicHuman Rights and Development
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMillennium Development GoalsPolitical scienceEconomic growthPoliticsWork (physics)Public relationsScale (ratio)Positive Youth DevelopmentDeveloping countryGeographyEngineeringEconomics
DOInot available

Abstract

fetched live from 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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.745
Threshold uncertainty score0.475

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.199
GPT teacher head0.351
Teacher spread0.152 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2005
Admission routes1
Has abstractyes

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