Quantifying the research impact of the Sustainable Forest Management Network in the social sciences: a bibliometric study
Bibliographic record
Abstract
This research note presents the results of a bibliometric analysis that was conducted to better understand the impact that Sustainable Forest Management Network (SFMN) funded research had in the forest-related social and Aboriginal research communities. We applied two indicators of research impact: (i) research outputs and (ii) citations. Our results suggest that the SFMN’s research outputs were highest in the fields of economics, sociology, and political science and law. The number of research articles that acknowledged the SFMN was 30% of the total research output of the SFMN-funded Principal Investigators. These articles represented 3% of the social science articles published in the Forestry Chronicle (the journal most frequently used by SFMN-funded Principal Investigators). Research output related to Aboriginal forestry indicated that the SFMN had a significant influence on the development of the field. Our citation analysis indicated that the average number of citations per SFMN-acknowledged publication in the social sciences was approximately the same as the international impact standard in the field. These results suggest that the SFMN-funded research in the social sciences compared very well with the international research standards in forest-related social sciences.
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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 | 0.034 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.073 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".