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Record W2153744473 · doi:10.26686/pq.v11i3.4546

The policy worker and the professor: understanding how New Zealand policy workers utilise academic research

2015· article· en· W2153744473 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenuePolicy Quarterly · 2015
Typearticle
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsPrime ministerGovernment (linguistics)Political sciencePublic administrationWork (physics)Research policyPublic policyManagementSociologyPublic relationsPoliticsLawEngineeringEconomics

Abstract

fetched live from OpenAlex

How do policy workers actually use academic research and advice? While there are several recent studies regarding this question from other Westminster jurisdictions (e.g. Talbot and Talbot, 2014, for the UK; Head et al., 2014, for Australia; Amara, Ouimet and Landry, 2004 and Ouimet et al., 2010, Canada), similar academic studies have been rare in New Zealand. So far, most of the local research in this field has been conducted by the prime minister’s chief science advisor and the Office of the Prime Minister’s Science Advisory Committee, with the particular instrumental purpose of improving the government’s ministries and agencies’ ‘use of evidence in both the formation and evaluation of policy’. However, none of these studies have asked how, and to what extent, policy workers in government are utilising academic research in their everyday work.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.021
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.873
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0210.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.574
GPT teacher head0.583
Teacher spread0.009 · 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