Collaboration via co-authorship trends in Government of Canada forestry research
Bibliographic record
Abstract
As part of its long history, the Canadian Forest Service (CFS) has a mandate to collaborate and share its scientific research. Publishing peer-reviewed scientific literature is an important part of this process. Using a database of CFS publications over the past fifty years, we highlight the continuing publication record of this sector of the Canadian government. The average number of authors reported in the CFS bookstore increased from 1.4 authors per article in the 1960s and 1.5 in the 1970s to just under five authors per publication from 2010 to 2018. Our work also illustrates challenges with longitudinal analysis of citation databases. In particular, use of a popular citation database resulted in significantly fewer articles authored by one person, and significantly more articles with twenty or more authors compared to the publicly available CFS “bookstore” of publications. Based on our findings, we outline a number of recommendations for use of citation data to inform collaboration research.
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 | 0.015 | 0.118 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.030 | 0.079 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".