The New Zealand Performance Based Research Fund and its Impact on Publication Activity in Economics
Bibliographic record
Abstract
New Zealand’s academic research assessment scheme, the Performance Based Research Fund (PBRF), was launched in 2002 with the stated objective of increasing research quality in the nation’s universities. Evaluation rounds were conducted in 2003, 2006 and 2012. In this paper, we employ 22 different journal weighting schemes to generate output estimates of refereed journal paper and page production over three six year periods (1994-1999; 2000-2005 and 2006-2011). These time 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 paper or page terms, has increased since the introduction of the PBRF. However, this outcome is due to a major increase in the quantity of papers 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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.125 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".