Publications as an Indicator of Increased Tobacco Control Research Productivity (Quantity and Quality) in New Zealand
Bibliographic record
Abstract
INTRODUCTION: Tobacco control (TC) research capacity and productivity are critical for developing evidence-informed interventions that will reduce the harmful effects of smoking. The aim of this paper was to investigate New Zealand's (NZ) TC research capacity along with the quantity and quality of publications, following two government initiatives aimed, in part, at improving the quantity and quality of NZ TC research. METHOD: Scopus was searched for articles with at least one NZ author and where the topic was of primary relevance to TC. Publications were organized into two time periods, following the government initiatives, 1993-2003 and 2004-2009. We analyzed the number of publications, publication journals, type of publications, impact (using the impact factor), and authorship. RESULTS: There has been an increase in number and impact of publications and number of authors. The number of publications has increased from an average of 14 (1994-2003) to 38 per year (2004-2009). The number of journals published increased from 64 to 86. The impact during 2004-2009 was almost threefold than in 1993-2003. The number of authors increased from 212 to 345, and the number of authors who had at least one first-authored publication increased from 80 to 124. CONCLUSIONS: These results show an encouraging trend in NZ TC research, with an increase in research productivity, quality, and in research capacity. It is possible that government-initiated and -funded infrastructural support contributed to increasing needed TC research, which supports the worth of such initiatives.
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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 | 0.024 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.028 | 0.040 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".