Government funding of health research in New Zealand.
Bibliographic record
Abstract
An analysis of levels of government health research funding carried out in 2008 demonstrated that funding in New Zealand, after adjustment for population size, was less than one-third of that in Australia, less than one-fifth of that in the United Kingdom, and about 10% of that in the United States. This was perceived to be a major obstacle to the recruitment and retention of clinical and academic staff in our hospitals and universities. We have now repeated these analyses to determine the current state of these comparisons. From 2009 to the present funds for direct funding of research through the Health Research Council (HRC) have remained static at $54m. As a result of inflation of research costs (principally salaries) this represents a decrease of approximately one-quarter in the quantum of research funded by the HRC over the last 4 years. Current funding rates in the comparator countries, population-adjusted and converted to NZ$, are 3.4-fold higher in Australia, 4.5-fold higher in the United Kingdom, and 9.7-fold higher in the United States. Urgent and sustained action is needed to correct these major disparities in government health research funding if the quality of academic and clinical staff in our public institutions is to be maintained.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".