Statistics on the papers published in the Canadian Journal of Pure and Applied Sciences from 2007 to 2021
Bibliographic record
Abstract
This paper reviews the articles published in the Canadian Journal of Pure and Applied Sciences (CJPAS) for the last fifteen years (15 volumes) from 2007 to 2021. The CJPAS authors relate to all the geographic places such as the North and South America, Europe and Asia, Oceania and Africa. Large contributions to this Journal are made by many authors of such countries as the Australia, Canada, USA, China, Russia, Japan, Great Britain, Switzerland, France, Italy, Ireland, Germany, Greece, Turkey, Brazil, Colombia, Korea, South Africa, NE Africa, Cameroun, Algeria, Morocco, Venezuela, Uruguay, United Arab Emirates, Saudi Arabia, Sultanate of Oman, Qatar, Kuwait, Malaysia, Indonesia, Taiwan, Fiji Islands, Egypt, Jordan, Israel, Iraq, Iran, Azerbaijan, India, Bangladesh, Pakistan, Sri Lanka, Macau, Ghana, and Nigeria. Since 2007 as many as 641 high-quality papers were published in the CJPAS, an international peer-reviewed Journal. On average it is ~ 43 papers annually and ~ 15 papers per issue (three issues per year in February, June and October) Most of the papers published in this multidisciplinary journal can be related to biology, chemistry, engineering, material sciences, informatics, mathematics, medicine, and physics. Using the last five years (volumes 11 to 15) the number of published papers per issue on average is ~ 9 and the average duration time between submission and acceptance of a paper is ~ 67 days, i.e. larger than two months.
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 | 0.009 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.055 | 0.081 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.039 | 0.023 |
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".