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Record W4412739144 · doi:10.1080/14754835.2025.2530101

Rights education and the children’s university

2025· article· en· W4412739144 on OpenAlexafffundabout
J. Marshall Beier

Bibliographic record

VenueJournal of Human Rights · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicChildren's Rights and Participation
Canadian institutionsMcMaster University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPolitical scienceHuman rightsLawSociologyCriminologyPsychology

Abstract

fetched live from OpenAlex

This article approaches children’s university programs as engaged forms of rights education allied with efforts toward democratic inclusion of children. To the extent they produce opportunities for children to discover themselves as participants in knowledge production and transmission, children’s universities promote children’s recognition of their own extant potential to make a difference in their societies. Meaningful participation, in turn, underwrites possibilities both for children to be seen as and to come to see themselves as practicing a fuller citizenship as children – premised on their present assets, capabilities, insights, and experiences, not just preparation for eventual ‘ascension’ to adulthood. The participation rights laid out in the United Nations Convention on the Rights of the Child (UNCRC) herald just this promise but, in practice, little progress has been made on their implementation in the more than three decades since the Convention came into force. Also largely unfulfilled is the UNCRC commitment for states to educate citizens (including but not limited to children) on the Convention and its provisions. Drawing from original research on children’s university models in Canada and Hawai‘i, I highlight the contributions of an ethos that positions children as acting subjects in knowledge practices, not merely a recipient audience.

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)0.005
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.046
Threshold uncertainty score0.104

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0120.038
Scholarly communication0.0090.008
Open science0.0010.014
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0120.001

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 head0.009
GPT teacher head0.293
Teacher spread0.285 · 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 designQualitative
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 routes3
Has abstractyes

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