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Record W4388958033 · doi:10.26504/rs170

Changing social and political attitudes in Ireland and Northern Ireland

2023· report· en· W4388958033 on OpenAlexafffund
James Laurence, Stefanie Sprong, Frances McGinnity, Helen Russell, Garance Hingre

Bibliographic record

Venuenot available
Typereport
Languageen
FieldSocial Sciences
TopicSocial Policy and Reform Studies
Canadian institutionsTrinity College
FundersQueen's UniversityQueen's University Belfast
KeywordsExcellenceCivil servantsNorthern irelandPolitical sciencePoliticsSustainabilityPublic administrationEconomic growthSociologyEthnologyLawEconomics

Abstract

fetched live from OpenAlex

The mission of the Economic and Social Research Institute (ESRI) is to advance evidence based policymaking that supports economic sustainability and social progress in Ireland.ESRI researchers apply the highest standards of academic excellence to challenges facing policymakers, focusing on 10 areas of critical importance to 21st Century Ireland.The Institute was founded in 1960 by a group of senior civil servants led by Dr T.K. Whitaker, who identified the need for independent and in-depth research analysis to provide a robust evidence base for policymaking in Ireland.Since then, the Institute has remained committed to independent research and its work is free of any expressed ideology or political position.The Institute publishes all research reaching the appropriate academic standard, irrespective of its findings or who funds the research.The quality of its research output is guaranteed by a rigorous peer review process.ESRI researchers are experts in their fields and are committed to producing work that meets the highest academic standards and practices.The work of the Institute is disseminated widely in books, journal articles and reports.ESRI publications are available to download, free of charge, from its website.Additionally, ESRI staff communicate research findings at regular conferences and seminars.The ESRI is a company limited by guarantee, answerable to its members and governed by a Council, comprising up to 14 members who represent a crosssection of ESRI members from academia, civil services, state agencies, businesses and civil society.The Institute receives an annual grant-in-aid from the Department of Public Expenditure and Reform to support the scientific and public interest elements of the Institute's activities; the grant accounted for an average of 30 per cent of the Institute's income over the lifetime of the last Research Strategy.The remaining funding comes from research programmes supported by government departments and agencies, public bodies and competitive research programmes.

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)0.003
metaresearch head score (Gemma)0.004
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 score0.253
Threshold uncertainty score0.504

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.136
GPT teacher head0.432
Teacher spread0.296 · 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

Citations2
Published2023
Admission routes2
Has abstractyes

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