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Record W4380767318 · doi:10.38126/jspg220203

Overlooked No More: Empowering Youth Voices in Global Climate-Change Negotiations

2023· article· en· W4380767318 on OpenAlexaff
Julian Campisi, Miriam Hird‐Younger, Evvan Morton, Hamangai Pataxó, F. Quispe, S. Nina, Laila Sandroni

Bibliographic record

VenueJournal of Science Policy & Governance · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change Communication and Perception
Canadian institutionsThe Scarborough HospitalCarleton UniversityUniversity of Toronto
FundersInter-American Institute for Global Change Research
KeywordsNegotiationPolitical scienceDiplomacyDiversity (politics)Inclusion (mineral)Latin AmericansPublic relationsClimate changeYouth engagementSociologyGender studiesPoliticsEcologyLaw

Abstract

fetched live from OpenAlex

Youth participation in climate change negotiations has increased over the last decade; however, youth voices are still underrepresented. The diversity of youth activists in the Americas and the sheer number of youth-led organizations belies any stereotypes about disinterested youth. Youth care about both the present and the future of our planet; are organizing; and have many voices. Yet, there are currently weak institutional mechanisms to integrate these voices into climate negotiations beyond showcasing experiences. Youth must be included in collaborative and transdisciplinary ways. We recommend opportunities that have had success in Latin America and the Caribbean (LAC), which help youth to engage in discussions with policymakers to inform climate negotiations. These recommendations include the following: 1) institutionalizing formal national and regional youth councils and committees to strengthen collaboration between young people and decision-makers; 2) creating and expanding training programs for youth on climate negotiations; 3) using science diplomacy as a key tool to enhance science-based, relevant, and collaborative efforts for youth engagement; and 4) developing strategies to navigate the diversity of expertise, scientific knowledge, and inclusion of youth to address equitable climate solutions.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.625
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.004
Science and technology studies0.0010.001
Scholarly communication0.0000.002
Open science0.0010.000
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 head0.276
GPT teacher head0.480
Teacher spread0.204 · 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 designObservational
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

Citations0
Published2023
Admission routes1
Has abstractyes

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