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Record W4402288304 · doi:10.1093/isp/ekae016

Forum: Youth as Boundary Actors in International Studies

2024· article· en· W4402288304 on OpenAlexaff
Amandine Orsini, Yi hyun Kang, Emmanuel Ampomah, Adam Cooper, Laura Gómez‐Mera, Brian Gran, Anna Holzscheiter, Roberto S. Salva, Anaëlle Vergonjeanne

Bibliographic record

VenueInternational Studies Perspectives · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicTourism, Volunteerism, and Development
Canadian institutionsQueen's University
FundersFonds De La Recherche Scientifique - FNRS
KeywordsPolitical scienceBoundary (topology)International relationsLawPoliticsMathematics

Abstract

fetched live from OpenAlex

Abstract Youth represent a great part of humanity and have always been active and intriguing political actors, yet youth remain sidelined in international studies. Issues of social identity perception and its consequences have been embraced by post-positivist approaches in international studies. Yet, while race, gender, and class challenges are shaking the discipline, age is a key research gap. To fill this gap, the conceptual departure of this forum is to study youth, taking 16–30/35 as an age range, as “boundary actors” in international politics. We assembled contributions that address this conceptual departure on topics, including health, conflict, climate change, and indigenous people’s rights, across all world regions with specific focuses on Africa and Asia. Overall, the forum demonstrates that youth are able to move the boundaries: (i) of norms in international politics by asking for a more inclusive implementation of human rights and/or environmental justice; (ii) of procedures by suggesting to broaden decision-making; (iii) of international activism by combining social media and protests as new strategies. Taken together, the contributions show that youth have and are a world-building project, not just a world-confirming project.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.090

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0080.006
Scholarly communication0.0080.007
Open science0.0010.015
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0200.001

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 head0.045
GPT teacher head0.390
Teacher spread0.345 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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
Published2024
Admission routes1
Has abstractyes

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