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Record W2091428252 · doi:10.1093/reseval/rvt017

The New Zealand performance-based research fund and its impact on publication activity in economics

2013· article· en· W2091428252 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueResearch Evaluation · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicNew Zealand Economic and Social Studies
Canadian institutionsQueen's University
FundersPennington Biomedical Research Foundation
KeywordsQueen (butterfly)Library scienceManagementSociologyMedia studiesEconomicsComputer science

Abstract

fetched live from OpenAlex

New Zealand’s academic research assessment scheme, the Performance-Based Research Fund (PBRF), was launched in 2002 with the stated objective of increasing average research quality in the nation’s universities. Evaluation rounds were conducted in 2003, 2006 and 2012. In this article, we use 22 different journal weighting schemes to generate output estimates of refereed journal article and page production for three 6-year periods (1994–9; 2000–5 and 2006–11). These periods reflect a pre-PBRF environment, a mixed assessment period, and a pure PBRF research environment, respectively. Our findings indicate that, on average, research productivity, defined in either article or page terms, has increased since the introduction of the PBRF. However, this outcome is due to a major increase in the quantity of articles and pages produced per capita that has more than off-set a decline in the quality of published outputs since the introduction of the PBRF. In other words, our findings suggest that the PBRF has failed to achieve its stated goal of increasing average research quality, but it has resulted in substantial gains in productivity achieved via large increases in the quantity of refereed journal articles.

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.

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaMetaresearchBibliometrics
Domain: Incentives · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptMetaresearchBibliometrics
Domain: Incentives · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalmedium
models agreeAgreement compares identical category sets and study designs across arms.

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)0.013
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.455
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.410
GPT teacher head0.438
Teacher spread0.028 · 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