Arctic Research Trends: External Funding 2016-2022
Bibliographic record
Abstract
This work was conducted by the University of the Arctic (UArctic) Thematic Network on Research Analytics and Bibliometrics. It was supported by Global Affairs Canada through the Global Arctic Leadership Initiative. The aim of this work is to follow up on previous analyses presented by the UArctic Science & Research Analytics Task Force, i.e. the pilot report “International Arctic Research – Analyzing Global Funding Trends, A Pilot report” (Osipov et al 2016) covering the period 1996-2015 with a specific focus on 2006–2015. The pilot report, published in 2016, and a close in time follow up report (Osipov et al 2017) were the first-ever attempts at creating a comprehensive view of global Arctic research funding using a dataset of such magnitude. This report is a “refresh” of these two original analyses, showing new data both from funding as well as time scope viewpoints. As in the pilot report, special attention has been given to describing and partly analyzing trends in the countries of the Arctic Council – both members (1) and observers (2) – as well as their key funding agencies and institutional members of the University of the Arctic. The results presented in this report share many similarities with those in the previous ones (Osipov et al 2016 & 2017). However, we consider the data more comprehensive, due to the maturing of Dimensions database used to identify funding sources over the years (3). Still, due to large differences in funding systems between different countries, the interpretation of funding trends must always be done with caution. (1) Canada, Finland, Iceland, Kingdom of Denmark, Norway, Russian Federation, Sweden, United States of America.(2) France, Germany, Italian Republic, Japan, the Netherlands, People's Republic of China, Poland, Republic of India, Republic of Korea, Republic of Singapore, Spain, United Kingdom.(3) https://www.digital-science.com/product/dimensions/
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.191 | 0.012 |
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".