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Record W2396091084 · doi:10.1080/03054985.2016.1184868

Shared education in Northern Ireland: school collaboration in divided societies

2016· article· en· W2396091084 on OpenAlexfundno aff
Tony Gallagher

Bibliographic record

VenueOxford Review of Education · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicReligious Education and Schools
Canadian institutionsnot available
FundersEconomic and Social Research CouncilOffice of the First Minister and Deputy First MinisterQueen's UniversityNorthern Ireland Community Relations CouncilQueen's University BelfastAtlantic Philanthropies
KeywordsNorthern irelandGovernment (linguistics)Variety (cybernetics)PoliticsSociologyProtestantismPublic relationsPublic administrationPolitical sciencePedagogyEconomic growthLaw

Abstract

fetched live from OpenAlex

During the years of political violence in Northern Ireland many looked to schools to contribute to reconciliation. A variety of interventions were attempted throughout those years, but there was little evidence that any had produced systemic change. The peace process provided an opportunity for renewed efforts. This paper outlines the experience of a series of projects on 'shared education', or the establishment of collaborative networks of Protestant, Catholic and integrated schools in which teachers and pupils moved between schools to take classes and share experiences. The paper outlines the genesis of the idea and the research which helped inform the shape of the shared education project. The paper also outlines the corpus of research which has examined various aspects of shared education practice and lays out the emergent model which is helping to inform current government practice in Northern Ireland, and is being adopted in other jurisdictions. The paper concludes by looking at the prospects for real transformation of education in Northern Ireland.

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)0.011
metaresearch head score (Gemma)0.015
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.158

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0100.025
Scholarly communication0.0100.008
Open science0.0030.031
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0070.001

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 head0.014
GPT teacher head0.361
Teacher spread0.347 · 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

Citations74
Published2016
Admission routes1
Has abstractyes

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Same venueOxford Review of EducationSame topicReligious Education and SchoolsFrench-language works237,207