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Record W4225004526 · doi:10.26504/rs138

A North-South comparison of education and training systems: lessons for policy

2022· report· en· W4225004526 on OpenAlexfundno aff
Emer Smyth, Anne Devlin, Adele Bergin, Séamus McGuinness

Bibliographic record

Venuenot available
Typereport
Languageen
FieldSocial Sciences
TopicEducation Systems and Policy
Canadian institutionsnot available
FundersQueen's UniversityQueen's University Belfast
KeywordsNorthern irelandPolitical scienceTraining (meteorology)Education policyPublic administrationGeographyPublic relationsHigher educationSociology

Abstract

fetched live from OpenAlex

This study draws on international and national survey data, administrative data, 31 interviews with 35 policy stakeholders and input from a consultation with stakeholders to document commonalities and differences between the education systems in Ireland and Northern Ireland. Remarkably, it is the first study to systematically compare the systems from primary to tertiary levels and it is hoped that the findings will provide insights for future policy learning in both jurisdictions. The study is timely given the independent review of education being conducted in Northern Ireland and ongoing reviews, especially at primary and upper secondary levels, in 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)0.020
metaresearch head score (Gemma)0.029
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 score0.088
Threshold uncertainty score0.174

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.029
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.006
Science and technology studies0.0020.004
Scholarly communication0.0060.012
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.000

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 head0.292
GPT teacher head0.516
Teacher spread0.224 · 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

Citations14
Published2022
Admission routes1
Has abstractyes

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