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Record W7111946349

An evidence-based approach to creating a culture of inclusive opportunity through arts and creativity

2025· article· en· W7111946349 on OpenAlexfundno aff

Bibliographic record

VenueDurham Research Online (Durham University) · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicArt Education and Development
Canadian institutionsnot available
FundersNewcastle UniversityUniversity of WinchesterBath Spa UniversityTeesside UniversityDurham UniversityYork UniversityUniversity of BradfordEdge Hill UniversityUniversity of LeedsOhio State University
KeywordsCreativityDisadvantagedThe artsAttendanceCurriculum
DOInot available

Abstract

fetched live from OpenAlex

The final report in a twelve-part series campaigning for a country that works for all children and young people. #ChildrenFirstProduced jointly by Child of the North and the Centre for Young Lives, the twelfth and final report in the Child of the North 2024/25 campaign series warns the talents of millions of children are being ignored and wasted, and calls for creativity to be embedded into an inclusive school curriculum supporting all children – including those with SEND – to develop a new generation of creatives to boost economic growth.First published in March 2025, An evidence-based approach to creating a culture of inclusive opportunity through arts and creativity, calls for the Government’s Opportunity Mission to boost culture, creativity, and arts in schools to inspire children, improve mental health, strengthen school belonging, tackle the school attendance and attainment crises, and support children from the most disadvantaged backgrounds to build careers in the creative industries.

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)0.184
metaresearch head score (Gemma)0.243
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.184
Threshold uncertainty score0.972

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1840.243
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0130.006
Science and technology studies0.0040.008
Scholarly communication0.0170.009
Open science0.0060.010
Research integrity0.0080.013
Insufficient payload (model declined to judge)0.0100.002

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 head0.207
GPT teacher head0.399
Teacher spread0.192 · 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 designTheoretical or conceptual
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
Published2025
Admission routes1
Has abstractyes

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