D4.2 White paper on European Polar Research funding landscape and cooperation potential
Bibliographic record
Abstract
This White Paper provides an analysis of the Directory of polar research funding programmes in Europe (D4.1) for the purpose of assisting European funding agencies and other stakeholders to understand the landscape and cooperation potential of European polar research funding. On this basis a continued dialogue with national polar research funding agencies and operators, EC representatives and other stakeholders, EU-PolarNet 2 will work towards developing the European Polar Research Area by optimising the coordination and complementary value of European (Horizon Europe), national and global polar research funding programmes. This is also needed to effectively address the most pressing research questions identified by the international polar research community under the SCAR Horizon Scan, the IASC ICARP III process and the scientific prioritisation of polar research topics identified under the Set of White papers addressing priority questions in polar research and targeting funding agencies and policy makers (EU-Polar Net 1 White Papers), as well as the Integrated European Polar Research Programme, published by EU-PolarNet 1 in 2020. After consultations and dialogue with funding agencies and stakeholders, our final goal under EU-PolarNet 2 is to provide recommendations for a Partnership initiative in Polar Research under Horizon Europe supporting the implementation and development of future European research actions.
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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 | 0.021 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.022 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.009 | 0.004 |
| Insufficient payload (model declined to judge) | 0.036 | 0.016 |
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".