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Record W7065277589

Economic inactivity in Northern Ireland

2023· article· en· W7065277589 on OpenAlexafffund

Bibliographic record

VenueResearch Portal (Queen's University Belfast) · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsQueen's University
FundersQueen's UniversityQueen's University BelfastDepartment for the Economy
KeywordsHeadlineNorthern irelandWork (physics)PandemicQualitative researchFace (sociological concept)Focus groupFalling (accident)
DOInot available

Abstract

fetched live from OpenAlex

Economic inactivity has historically been high in Northern Ireland (NI) when compared to the other United Kingdom (UK) countries. The following Research Bulletin sheds light on this phenomenon by drawing on recent research conducted by the Economic & Social Research Institute (ESRI) and Queen’s University Belfast (QUB) on behalf of the Department for the Economy. The Bulletin examines both headline inactivity rates and more granular patterns of inactivity (i.e., heterogeneity between inactivity forms). Inactivity patterns in NI are compared with the UK average and Ireland. The Bulletin also examines inactivity by gender and age, and provides a focus on post-Covid-19 pandemic inactivity rates. Finally, qualitative work is drawn upon to shed light on reasons behind the high rates of inactivity, providing insights into different barriers people face in accessing the labour market.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.312
Threshold uncertainty score0.620

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0000.001
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 head0.016
GPT teacher head0.273
Teacher spread0.257 · 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 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

Citations0
Published2023
Admission routes2
Has abstractyes

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