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Record W7161962447 · doi:10.1093/ips/olaf037

The Adult Gaze: Looking Again at Children and Young People in Peace and Conflict

2025· article· en· W7161962447 on OpenAlexaff
Alice König, Rebecca Sutton, Jana Tabak, Ali Altiok, Tugçe Ataci, J Marshall Beier, Mehmet Ilhanli, Patrícia Nabuco Martuscelli

Bibliographic record

VenueInternational Political Sociology · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Security, and Conflict
Canadian institutionsMcMaster University
Fundersnot available
KeywordsForegroundingConversationInclusion (mineral)PoliticsWork (physics)Childhood studiesDual (grammatical number)Youth work

Abstract

fetched live from OpenAlex

Abstract Young people are often talked about as “the future,” with the dual implication that they will inherit the results of contemporary decision-making and direct policy-making once they reach adulthood. However, it is not only in the future that they will encounter, have to think about, suffer from, and develop expertise in conflict. Building on a growing body of work considering young voices in peace and conflict studies and their participation as future-makers in global politics, this collective discussion looks at where children, youth, and adults meet in this endeavor—and at how conceptions of childhood (as distinct from actual children) can work against such dialogue. We explore what both young people and adults bring to intergenerational exchanges of experience and expertise and how adult ideas of childhood and adult frameworks of inclusion can enable or constrain young people’s contributions to discourses on war and peace. We also disentangle adulthood/adultism from adults, foregrounding the positive roles that the latter can play in dismantling the former. Our goal is to stimulate further conversation about how the barriers to young people’s inclusion in the politics of war and peace are produced, perpetuated, and resisted in the processes of ordering everyday political life.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.614
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.011
GPT teacher head0.319
Teacher spread0.308 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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