The Relevance of the ‘h’ and ‘g’ Index to Economics in the Context of a Nation-wide Research Evaluation Scheme: The New Zealand Case
Bibliographic record
Abstract
The purpose of this paper is to explore the relevance of the citation-based ‘h’ and ‘g’ indexes as a means for measuring research output in economics. This study is unique in that it is the first to utilize the ‘h’ and ‘g’ indexes in the context of a time limited evaluation period and to provide comprehensive coverage of all academic economists in all university-based economics departments within a nation state. For illustration purposes we have selected New Zealand’s Performance Based Research Fund (PBRF) as our evaluation scheme. In order to provide a frame of reference for ‘h’ and ‘g’ index output measures, we have also estimated research output using a number of journal-based weighting schemes. In general, our findings suggest that ‘h’ and ‘g’ index scores are strongly associated with low-powered journal ranking schemes and weakly associated with high powered journal weighting schemes. More specifically, we found the ‘h’ and ‘g’ indexes to suffer from a lack of differentiation: for example, 52 percent of all participants received a score of zero under both measures, and 92 and 89 percent received scores of two or less under ‘h’ and ‘g’, respectively. Overall, our findings suggest that ‘h’ and ‘g’ indexes should not be incorporated into a PBRF-like framework.
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 | 0.047 | 0.193 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.018 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".