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Record W1517310588 · doi:10.1108/13683040210441977

Delivering better government

2002· article· en· W1517310588 on OpenAlexaff
Eileen Drew

Bibliographic record

VenueMeasuring Business Excellence · 2002
Typearticle
Languageen
FieldSocial Sciences
TopicGender Diversity and Inequality
Canadian institutionsTrinity College
Fundersnot available
KeywordsIrishGovernment (linguistics)Context (archaeology)UnderpinningDiversity (politics)Service delivery frameworkQuality (philosophy)Public relationsBusinessPublic serviceHuman resourcesService (business)MarketingPublic administrationPolitical scienceManagementEconomicsEngineering

Abstract

fetched live from OpenAlex

This paper critically examines the Irish Government’s commitment to “Delivering better government”, in the context of achieving gender equality. The strategic management initiative (SMI) seeks highest quality of service delivery to customers in a modern flexible and professional manner. This necessitates the fullest development of human resources. To achieve this end a study was undertaken to investigate gender imbalance at managerial grades. The report highlighted continuing gender imbalance at all grades and a prevailing culture that is less than conducive to women and many men. The authors called for a new strategic approach to gender equality in which specific targets are set over a specific time frame. The Irish Civil Service is now faced with a combined need to address a new equality agenda in order to deliver its strategic vision of quality in serving the public, thereby underpinning the need to pursue quality, equality and diversity as core values in a fast‐growing Irish economy.

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)0.010
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.025
Threshold uncertainty score0.104

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0070.008
Scholarly communication0.0160.008
Open science0.0010.009
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0250.006

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 head0.181
GPT teacher head0.239
Teacher spread0.058 · 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 designNot applicable
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

Citations16
Published2002
Admission routes1
Has abstractyes

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