Evaluating research – peer review team assessment and journal based bibliographic measures: New Zealand PBRF research output scores in 2006
Bibliographic record
Abstract
This paper concerns the relationship between the assessment of the research of individual academics by peer or expert review teams with a variety of bibliometric schemes based on journal quality weights. Specifically, for a common group of economists from New Zealand departments of economics the relationship between Performance-Based Research Fund (PBRF) Research Output measures for those submitting new research portfolios in 2006 are compared with evaluations of journal-based research over the 2000–2005 assessment period. This comparison identifies the journal weighting schemes that appear most similar to PBRF peer evaluations. The paper provides an indication of the ‘power or aggressiveness’ of PBRF evaluations in terms of the weighting given to quality. The implied views of PBRF peer review teams are also useful in assessing common assumptions made in evaluating journal based research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.201 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.128 | 0.104 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.008 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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; both teacher heads agree on what is shown here.
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".