Introduction: Building a Circumpolar Innovation Agenda
Bibliographic record
Abstract
Governments have almost uniformly concluded that innovation is the key for long-term economic prosperity and for improvements in the quality of life for people around the world (e.g., Prime Minister's Offi ce, Finland 2015; Industry Canada 2017).New and adapted scientifi c and technological innovations have included, for example, nanofi ltration water fi lters that are producing clean drinking water for people in sub-Saharan Africa, high-speed wireless services for remote parts of Africa, GPS-based navigation systems that are improving transportation systems, social-media powered commercial operations from Airbnb to Uber, and medical technologies embedded in smartphones.However, litt le of this government supported and private-sector funded innovation eff ort has fi ltered through to the Circumpolar World.Northern regions often get later and smaller versions of southern innovations, with very few north-centred developments.For instance, while the Internet is generally available in all but the smallest and most remote communities, it is often characterized by minimal speeds, poor reliability, and extremely high costs (especially in northern Canada, Dobby 2016a; 2016b; FCM 2017).Thus, for the people of the Circumpolar World, the technological revolution has made comparatively few inroads.Equally important, the challenges facing this region have garnered signifi cantly less att ention from innovation stakeholders.Likewise, North America's and Europe's research universities, overwhelmingly near urban centres, receive and spend billions of dollars on nanotechnology, biotechnology, medical sciences, informational technologies, material sciences, environmental machines and systems,
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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 | 0.013 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".