An evaluation of forestry journals using bibliometric indices
Bibliographic record
Abstract
The increasing number of scientific journals, especially over the last 20 years, created the need for methodologies based on simple metrics, to accurately capture the “quality” of those journals and their impact on the scientific community. Especially in the case of journals from the field of forestry, relatively little work has been conducted on providing valid journal classifications. In this paper we attempt to assess the impact of journals from this field in terms of bibliometric data. In addition to the already proposed metrics (complementary to the journal h-index), we also apply a new measure to rank journals, that provides a more balanced evaluation of the journal performance, by adjusting for various biases affecting the h-index. We examined the relationships between various bibliometric indicators proposed for assessing the journal impact and wo found high correlations between most indices, with only few exceptions. According to citation analysis, Canadian Journal of Forest Research, Journal of Vegetation Science, Forest Science, Tree Physiology, International Journal of Wildland Fire, Holzforschung, Trees-Structure and Function, Silva Fennica, Agricultural and Forest Meteorology and Wood and Fiber Science are the top forestry journals. These publish articles related to all the domains of forestry science. More specialized journals are also included, dealing with specific issues of scientific interest and also of major importance to the scientific community.
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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 | 0.019 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.115 | 0.110 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".